Age differences in risk-taking propensity: A coordinated analysis of longitudinal panels
Yunrui Liu
Center for Cognitive and Decision Sciences, University of BaselAlexandra Bagaini
Center for Cognitive and Decision Sciences, University of BaselMadlaina Kapoor
Center for Cognitive and Decision Sciences, University of BaselGayoung Son
Center for Cognitive and Decision Sciences, University of BaselRui Mata
Center for Cognitive and Decision Sciences, University of Basel1. Overview of Project
How does risk-taking propensity change across the life span? We contribute to answering this question using a coordinated analysis of longitudinal panels and obtaining meta-analytic estimates of age differences in risk-taking propensity across several domains. Specifically, we report results from 10 longitudinal panels (24 samples; 169845 unique respondents) covering general and domain-specific risk-taking propensity (financial, driving, recreational, occupational, health, social) across three or more waves spanning up to 28 years. The meta-analytic results revealed a negative relation between age and both general and domain-specific risk-taking propensity (EFFECT PER DECADE?). Age differences, however, were more pronounced in specific domains, with age showing larger negative effects in the recreational and occupational domains. This work suggests there is need to understand the domain-specific nature of age differences in risk-taking propensity across the life span.
2. Introduction of the file
The following document contains results from all analyses conducted for the manuscript titled “Trajectories of Risk-taking Propensity: A Coordinated Analysis of Longitudinal Panels”. This document is organized by different domain risk-taking propensity,including general, driving, financial, recreational, occupational, health and social domain. For each risk-taking propensity, we create 7 models (including intercept-only model, fixed effect model, linear model, linear with gender model, linear with gender interaction model, quadratic model and quadratic with gender model) and provide a table summarizing individual study model results, the meta-analysis results and trajectory plots. We also tested individual predictors that are not included in the simple trajectory model in meta regression: continent, mean age and scale range. And the results from these models are available below. The code used to compile this file is available here (insert Github link)
3. Overview of panel data
3.1 The number of observations
3.2 Histogram of age distributions (all observations)
3.3 Histograms and Density Plots for every panel
This section offers a detailed overview of the different samples included in the analyses of the paper Age differences in risk-taking propensity: A coordinated analysis of longitudinal panels.
Each panel is described in a separate tab. We include the following:
Panel name. Full name of the panel.
Description. This is a general description of the objectives of the panel.
Country/Countries. Country or countries in which data are collected.
Waves. Waves available in the raw data set (not all waves were necessarily included in the data analysis as not every wave had collected data on the variables of interest)
Data collection period. Data collection period of the waves available in the raw data set.
Dataset(s) version number/name. Version number(s) or name(s) or raw dataset(s).
Data access. Link to directly access or request access to the raw dataset(s).
Age distribution. The density of each age and the number of observations in each age-bin(s).
Risk-taking propensity density. The raw score and standard Z-score risk-taking propensity density in every domain(s).
DNB
Panel Name: DNB Household Survey (DNB)
Description: The DNB Household Survey, undertaken by CentERdata at Tilburg University since 1993, provides annual financial information on 2,000 Dutch households. DNB Household Survey topics include: work, pensions, accommodation, mortgages, income, assets, liabilities, health, perception of personal financial situation and perception of risks.
More information at: (homepage)[link]
Country/Countries: Netherlands
Waves: 1993-2020
Data collection period: 1993-2020
Dataset(s) version number/name: NA
Data access: https://www.dhsdata.nl/site/users/login
Age distribution
Risk-taking propensity density:
Financial
GCOE_Japan
Panel Name: Preference Parameters Study (GCOE) Japan Sample
Description: The Preference Parameters Study of Osaka University is an extensive panel study in 4 different countries (Japan, United States, China and India). It aims to caculate parameters of preferences defining utility function; time preference, risk aversion, habit formation, externality, as well as sociodemographic characteristics. In China and India, surveys were conducted separately in urban and rural areas.
The panel survey in Japan has been conducted annually since 2003 using a random sample drawn from men and women aged 20-69 years old by a self-administered placement method. Fresh samples were selected and added in respondents to the survey for wave 2004, 2006 and 2009.
More information at: https://www.iser.osaka-u.ac.jp/survey_data/eng_panelsummary.html
Country/Countries: Japan
Waves: 2004-2010
Data collection period: 2003-2018
Dataset(s) version number/name: NA
Data access: https://www.iser.osaka-u.ac.jp/survey_data/eng_application.html
Age distribution:
Risk-taking propensity density:
General
GCOE_USA
Panel Name: Preference Parameters Study (GCOE) USA Sample
Description: The Preference Parameters Study of Osaka University is an extensive panel study in 4 different countries (Japan, United States, China and India). It aims to caculate parameters of preferences defining utility function; time preference, risk aversion, habit formation, externality, as well as sociodemographic characteristics. In China and India, surveys were conducted separately in urban and rural areas.
The panel survey for the GCOE USA sample has been conducted annually since 2005 using a random sample drawn from men and women aged 18-99 years old by a self-administered placement method. Fresh samples were selected and added in respondents to the survey for wave 2007, 2008 and 2009.
More information at: https://www.iser.osaka-u.ac.jp/survey_data/eng_panelsummary.html
Country/Countries: United States
Waves: 2005-2010
Data collection period: 2005-2013
Dataset(s) version number/name: NA
Data access: https://www.iser.osaka-u.ac.jp/survey_data/eng_application.html
Age distribution:
Risk-taking propensity density:
General
HILDA
Panel Name: Household, Income and Labour Dynamics in Australia (HILDA)
Description: The Household, Income and Labour Dynamics in Australia (HILDA) Survey is a household-based panel study that collects information about economic and personal well-being, labour market dynamics and family life of participants. Since 2001, the study has been following more than 17,000 Australian participants each year.
More information at: https://melbourneinstitute.unimelb.edu.au/hilda
Country/Countries: Australia
Waves: Wave I - Wave 19
Data collection period: 2001-present
Dataset(s) version number/name: NA
Data access: https://melbourneinstitute.unimelb.edu.au/hilda/for-data-users
Age distribution
Risk-taking propensity density:
Financial
HRS
Panel Name: Health and Retirement Study (HRS)
Description: The Health and Retirement Study (HRS) is a longitudinal panel study that surveys a representative sample of approximately 20,000 people in America. The target population for the first wave of the HRS was adults residing in households in the contiguous United States born between 1931 and 1941 (i.e., those who were between the ages of 51–61 in 1992 when the study began). One particular strength of the HRS sample design is the use of a steady-state sampling design: a new cohort of individuals age 51–56 is added every 6 years. Individuals and their spouses or partners are followed until their death. Data have been collected biannually since 1992.
More information at: https://hrs.isr.umich.edu/about
Country/Countries: United States
Waves: 2014-2018
Data collection period: 1984-present
Dataset(s) version number/name: Core Waves 1992-2018
Data access: https://hrsdata.isr.umich.edu/data-products/public-survey-data
Age distribution:
Risk-taking propensity density:
General
Driving
Financial
Recreational
Occupational
Health
LIKS
Panel Name: Life in Kyrgyzstan (LIKS)
Description: The ‘Life in Kyrgyzstan’ Study is a longitudinal survey of households and individuals in Kyrgyzstan. It tracks the same 3,000 households and 8,000 individuals over time in all seven Kyrgyz regions (oblasts) and the two cities of Bishkek and Osh. The data are representative nationally and at the regional level (East, West, North, South). The survey interviews all adult household members about household demographics, assets, expenditure, migration, employment, agricultural markets, shocks, social networks, subjective well-being, and many other topics. Some of these topics are addressed in each wave while other topics are only addressed in selected waves. All members of the households in 2010 are tracked for each wave and new household members are added to the survey and tracked as well. The survey was first conducted in 2010 and it has been repeated four times in 2011, 2012, 2013 and 2016. The sixth wave of the LiK Study was conducted during November 2019-February 2020.
More information at: https://lifeinkyrgyzstan.org/about/
Country/Countries: Kyrgyzstan
Waves: 2010, 2011, 2012, 2013, 2016
Data collection period: 2010-present
Dataset(s) version number/name: NA
Data access: https://lifeinkyrgyzstan.org/data-access/
Age distribution:
Risk-taking propensity density:
General
PHF
Panel Name: Panel on Household Finances (PHF)
Description: The German Panel on Household Finances (PHF) is a panel survey on household finance and wealth in Germany, covering the balance sheet, pension, income, work life and other demographic characteristics of private households living in Germany. The first wave of the PHF was carried out in 2010/2011, the second and third wave in 2014 and 2017, respectively. In the first wave, around 3,500 randomly selected households participated, from which about 2,200 also participated in the second wave.The fourth wave is schedules to start in spring 2021.
More information at: https://www.bundesbank.de/en/bundesbank/research/panel-on-household-finances
Country/Countries: Germany
Waves: Wave 1-Wave 3
Data collection period: 2010-present
Dataset(s) version number/name: NA
Data access: https://www.bundesbank.de/en/bundesbank/research/panel-on-household-finances/data-access-and-data-protection
Age distribution:
Risk-taking propensity density:
General
Financial
SAVE
Panel Name: Sparen und Altersvorsorge in Deutschland (SAVE)
Description: The Sparen und Altersvorsorge in Deutschland (SAVE) is a representative, longitudinal study on households’ financial behavior with a special focus on savings and old-age provision. Started in 2001, SAVE has collected data on households’ financial structure and relevant socio- and psychological aspects until 2013.
More information at: https://www.mpisoc.mpg.de/en/social-policy-mea/research/save-2001-2013/
Country/Countries: Germany
Waves: 2001-2013
Data collection period: 2001-2013
Dataset(s) version number/name: NA
Data access: https://dbk.gesis.org/dbksearch/GDESC2.asp?no=0014&search=save&search2=&DB=d&tab=0¬abs=&nf=1&af=&ll=10
Age distribution:
Risk-taking propensity density:
Driving
Financial
Recreational
Occupational
Health
SHARE_Austria
Panel Name: Survey of Health, Ageing and Retirement in Europe (SHARE) Austria Sample
Description: The Survey of Health, Ageing and Retirement in Europe (SHARE) is a research infrastructure for studying the effects of health, social, economic and environmental policies over the life-course of European citizens and beyond. From 2004 until today, 140,000 people aged 50 or older from 28 European countries and Israel have been interviewed in 7 waves. SHARE is the largest pan-European social science panel study providing internationally comparable longitudinal micro data which allow insights in the fields of public health and socio-economic living conditions of European individuals.
More information at: http://www.share-project.org/home0.html
Country/Countries: Austria
Waves:2007, 2011, 2013, 2015, 2017, 2019
Data collection period: 2007, 2011, 2013, 2015, 2017, 2019
Dataset(s) version number/name: NA
Data access: http://www.share-project.org/data-access.html
Age distribution:
Risk-taking propensity density:
Financial
SHARE_Austria
Panel Name: Survey of Health, Ageing and Retirement in Europe (SHARE) Austria Sample
Description: The Survey of Health, Ageing and Retirement in Europe (SHARE) is a research infrastructure for studying the effects of health, social, economic and environmental policies over the life-course of European citizens and beyond. From 2004 until today, 140,000 people aged 50 or older from 28 European countries and Israel have been interviewed in 7 waves. SHARE is the largest pan-European social science panel study providing internationally comparable longitudinal micro data which allow insights in the fields of public health and socio-economic living conditions of European individuals.
More information at: http://www.share-project.org/home0.html
Country/Countries: Austria
Waves:2007, 2011, 2013, 2015, 2017, 2019
Data collection period: 2007, 2011, 2013, 2015, 2017, 2019
Dataset(s) version number/name: NA
Data access: http://www.share-project.org/data-access.html
Age distribution:
Risk-taking propensity density:
Financial
SHARE_Belgium
Panel Name: Survey of Health, Ageing and Retirement in Europe (SHARE) Belgium Sample
Description: The Survey of Health, Ageing and Retirement in Europe (SHARE) is a research infrastructure for studying the effects of health, social, economic and environmental policies over the life-course of European citizens and beyond. From 2004 until today, 140,000 people aged 50 or older from 28 European countries and Israel have been interviewed in 7 waves. SHARE is the largest pan-European social science panel study providing internationally comparable longitudinal micro data which allow insights in the fields of public health and socio-economic living conditions of European individuals.
More information at: http://www.share-project.org/home0.html
Country/Countries: Belgium
Waves:2007, 2011, 2013, 2015, 2017, 2019
Data collection period: 2007, 2011, 2013, 2015, 2017, 2019
Dataset(s) version number/name: NA
Data access: http://www.share-project.org/data-access.html
Age distribution:
Risk-taking propensity density:
Financial
SHARE_Czech_Republic
Panel Name: Survey of Health, Ageing and Retirement in Europe (SHARE) Czech Republic Sample
Description: The Survey of Health, Ageing and Retirement in Europe (SHARE) is a research infrastructure for studying the effects of health, social, economic and environmental policies over the life-course of European citizens and beyond. From 2004 until today, 140,000 people aged 50 or older from 28 European countries and Israel have been interviewed in 7 waves. SHARE is the largest pan-European social science panel study providing internationally comparable longitudinal micro data which allow insights in the fields of public health and socio-economic living conditions of European individuals.
More information at: http://www.share-project.org/home0.html
Country/Countries: Czech Republic
Waves:2007, 2011, 2013, 2015, 2017
Data collection period: 2007, 2011, 2013, 2015, 2017, 2019
Dataset(s) version number/name: NA
Data access: http://www.share-project.org/data-access.html
Age distribution:
Risk-taking propensity density:
Financial
SHARE_Denmark
Panel Name: Survey of Health, Ageing and Retirement in Europe (SHARE) Denmark Sample
Description: The Survey of Health, Ageing and Retirement in Europe (SHARE) is a research infrastructure for studying the effects of health, social, economic and environmental policies over the life-course of European citizens and beyond. From 2004 until today, 140,000 people aged 50 or older from 28 European countries and Israel have been interviewed in 7 waves. SHARE is the largest pan-European social science panel study providing internationally comparable longitudinal micro data which allow insights in the fields of public health and socio-economic living conditions of European individuals.
More information at: http://www.share-project.org/home0.html
Country/Countries: Denmark
Waves:2007, 2011, 2013, 2015, 2017
Data collection period: 2007, 2011, 2013, 2015, 2017, 2019
Dataset(s) version number/name: NA
Data access: http://www.share-project.org/data-access.html
Age distribution:
Risk-taking propensity density:
Financial
SHARE_Estonia
Panel Name: Survey of Health, Ageing and Retirement in Europe (SHARE) Estonia Sample
Description: The Survey of Health, Ageing and Retirement in Europe (SHARE) is a research infrastructure for studying the effects of health, social, economic and environmental policies over the life-course of European citizens and beyond. From 2004 until today, 140,000 people aged 50 or older from 28 European countries and Israel have been interviewed in 7 waves. SHARE is the largest pan-European social science panel study providing internationally comparable longitudinal micro data which allow insights in the fields of public health and socio-economic living conditions of European individuals.
More information at: http://www.share-project.org/home0.html
Country/Countries: Estonia
Waves:2011, 2013, 2015
Data collection period: 2011, 2013, 2015, 2019
Dataset(s) version number/name: NA
Data access: http://www.share-project.org/data-access.html
Age distribution:
Risk-taking propensity density:
Financial
SHARE_France
Panel Name: Survey of Health, Ageing and Retirement in Europe (SHARE) France Sample
Description: The Survey of Health, Ageing and Retirement in Europe (SHARE) is a research infrastructure for studying the effects of health, social, economic and environmental policies over the life-course of European citizens and beyond. From 2004 until today, 140,000 people aged 50 or older from 28 European countries and Israel have been interviewed in 7 waves. SHARE is the largest pan-European social science panel study providing internationally comparable longitudinal micro data which allow insights in the fields of public health and socio-economic living conditions of European individuals.
More information at: http://www.share-project.org/home0.html
Country/Countries: France
Waves: 2007, 2011, 2013, 2015, 2017
Data collection period: 2007, 2011, 2013, 2015, 2017, 2019
Dataset(s) version number/name: NA
Data access: http://www.share-project.org/data-access.html
Age distribution:
Risk-taking propensity density:
Financial
SHARE_Germany
Panel Name: Survey of Health, Ageing and Retirement in Europe (SHARE) Germany Sample
Description: The Survey of Health, Ageing and Retirement in Europe (SHARE) is a research infrastructure for studying the effects of health, social, economic and environmental policies over the life-course of European citizens and beyond. From 2004 until today, 140,000 people aged 50 or older from 28 European countries and Israel have been interviewed in 7 waves. SHARE is the largest pan-European social science panel study providing internationally comparable longitudinal micro data which allow insights in the fields of public health and socio-economic living conditions of European individuals.
More information at: http://www.share-project.org/home0.html
Country/Countries: Germany
Waves: 2007, 2011, 2013, 2015, 2017, 2019
Data collection period: 2007, 2011, 2013, 2015, 2017, 2019
Dataset(s) version number/name: NA
Data access: http://www.share-project.org/data-access.html
Age distribution:
Risk-taking propensity density:
Financial
SHARE_Israel
Panel Name: Survey of Health, Ageing and Retirement in Europe (SHARE) Israel Sample
Description: The Survey of Health, Ageing and Retirement in Europe (SHARE) is a research infrastructure for studying the effects of health, social, economic and environmental policies over the life-course of European citizens and beyond. From 2004 until today, 140,000 people aged 50 or older from 28 European countries and Israel have been interviewed in 7 waves. SHARE is the largest pan-European social science panel study providing internationally comparable longitudinal micro data which allow insights in the fields of public health and socio-economic living conditions of European individuals.
More information at: http://www.share-project.org/home0.html
Country/Countries: Israel
Waves: 2007, 2013, 2015
Data collection period: 2007, 2013, 2015, 2019
Dataset(s) version number/name: NA
Data access: http://www.share-project.org/data-access.html
Age distribution:
Risk-taking propensity density:
Financial
SHARE_Italy
Panel Name: Survey of Health, Ageing and Retirement in Europe (SHARE) Italy Sample
Description: The Survey of Health, Ageing and Retirement in Europe (SHARE) is a research infrastructure for studying the effects of health, social, economic and environmental policies over the life-course of European citizens and beyond. From 2004 until today, 140,000 people aged 50 or older from 28 European countries and Israel have been interviewed in 7 waves. SHARE is the largest pan-European social science panel study providing internationally comparable longitudinal micro data which allow insights in the fields of public health and socio-economic living conditions of European individuals.
More information at: http://www.share-project.org/home0.html
Country/Countries: Italy
Waves: 2007, 2011, 2013, 2015, 2017, 2019
Data collection period: 2007, 2011, 2013, 2015, 2017, 2019
Dataset(s) version number/name: NA
Data access: http://www.share-project.org/data-access.html
Age distribution:
Risk-taking propensity density:
Financial
SHARE_Netherlands
Panel Name: Survey of Health, Ageing and Retirement in Europe (SHARE) Netherlands Sample
Description: The Survey of Health, Ageing and Retirement in Europe (SHARE) is a research infrastructure for studying the effects of health, social, economic and environmental policies over the life-course of European citizens and beyond. From 2004 until today, 140,000 people aged 50 or older from 28 European countries and Israel have been interviewed in 7 waves. SHARE is the largest pan-European social science panel study providing internationally comparable longitudinal micro data which allow insights in the fields of public health and socio-economic living conditions of European individuals.
More information at: http://www.share-project.org/home0.html
Country/Countries: Netherlands
Waves: 2007, 2011, 2013, 2019
Data collection period: 2007, 2011, 2013, 2019
Dataset(s) version number/name: NA
Data access: http://www.share-project.org/data-access.html
Age distribution:
Risk-taking propensity density:
Financial
SHARE_Slovenia
Panel Name: Survey of Health, Ageing and Retirement in Europe (SHARE) Slovenia Sample
Description: The Survey of Health, Ageing and Retirement in Europe (SHARE) is a research infrastructure for studying the effects of health, social, economic and environmental policies over the life-course of European citizens and beyond. From 2004 until today, 140,000 people aged 50 or older from 28 European countries and Israel have been interviewed in 7 waves. SHARE is the largest pan-European social science panel study providing internationally comparable longitudinal micro data which allow insights in the fields of public health and socio-economic living conditions of European individuals.
More information at: http://www.share-project.org/home0.html
Country/Countries: Slovenia
Waves: 2011, 2013, 2015, 2019
Data collection period: 2011, 2013, 2015, 2019
Dataset(s) version number/name: NA
Data access: http://www.share-project.org/data-access.html
Age distribution:
Risk-taking propensity density:
Financial
SHARE_Spain
Panel Name: Survey of Health, Ageing and Retirement in Europe (SHARE) Spain Sample
Description: The Survey of Health, Ageing and Retirement in Europe (SHARE) is a research infrastructure for studying the effects of health, social, economic and environmental policies over the life-course of European citizens and beyond. From 2004 until today, 140,000 people aged 50 or older from 28 European countries and Israel have been interviewed in 7 waves. SHARE is the largest pan-European social science panel study providing internationally comparable longitudinal micro data which allow insights in the fields of public health and socio-economic living conditions of European individuals.
More information at: http://www.share-project.org/home0.html
Country/Countries: Spain
Waves: 2007, 2011, 2013, 2015, 2017
Data collection period: 2007, 2011, 2013, 2015, 2017, 2019
Dataset(s) version number/name: NA
Data access: http://www.share-project.org/data-access.html
Age distribution:
Risk-taking propensity density:
Financial
SHARE_Sweden
Panel Name: Survey of Health, Ageing and Retirement in Europe (SHARE) Sweden Sample
Description: The Survey of Health, Ageing and Retirement in Europe (SHARE) is a research infrastructure for studying the effects of health, social, economic and environmental policies over the life-course of European citizens and beyond. From 2004 until today, 140,000 people aged 50 or older from 28 European countries and Israel have been interviewed in 7 waves. SHARE is the largest pan-European social science panel study providing internationally comparable longitudinal micro data which allow insights in the fields of public health and socio-economic living conditions of European individuals.
More information at: http://www.share-project.org/home0.html
Country/Countries: Sweden
Waves: 2007, 2011, 2013, 2015, 2017
Data collection period: 2007, 2011, 2013, 2015, 2017, 2019
Dataset(s) version number/name: NA
Data access: http://www.share-project.org/data-access.html
Age distribution:
Risk-taking propensity density:
Financial
SHARE_Switzerland
Panel Name: Survey of Health, Ageing and Retirement in Europe (SHARE) Switzerland Sample
Description: The Survey of Health, Ageing and Retirement in Europe (SHARE) is a research infrastructure for studying the effects of health, social, economic and environmental policies over the life-course of European citizens and beyond. From 2004 until today, 140,000 people aged 50 or older from 28 European countries and Israel have been interviewed in 7 waves. SHARE is the largest pan-European social science panel study providing internationally comparable longitudinal micro data which allow insights in the fields of public health and socio-economic living conditions of European individuals.
More information at: http://www.share-project.org/home0.html
Country/Countries: Switzerland
Waves: 2007, 2011, 2013, 2015, 2017, 2019
Data collection period: 2007, 2011, 2013, 2015, 2017, 2019
Dataset(s) version number/name: NA
Data access: http://www.share-project.org/data-access.html
Age distribution:
Risk-taking propensity density:
Financial
SOEP
Panel Name: German Socio-Economic Panel (SOEP)
Description: The Socio-Economic Panel (SOEP) is one of the largest and longest-running multidisciplinary household surveys worldwide. Every year, approximately 30,000 people in 15,000 households are interviewed for the SOEP study. The SOEP is also a research-driven infrastructure based at DIW Berlin. The SOEP team prepares survey data for use by researchers around the globe, and team members use the data in research on various topics. Studies based on SOEP data examine diverse aspects of societal change.
More information at: https://www.diw.de/en/diw_01.c.600489.en/about_us.html#c_624242
Country/Countries: Germany
Waves: 2004-2018
Data collection period: 1984-present
Dataset(s) version number/name: SOEP-Core v35
Data access: https://www.diw.de/sixcms/detail.php?id=diw_01.c.742256.en
Age distribution:
Risk-taking propensity density:
General
Driving
Financial
Recreational
Occupational
Health
Social
Usoc
Panel Name: UK Household Longitudinal Survey (Understanding Society) (USOC)
Description: THe UK Household Longitudinal Study/Understanding Society (USOC) is built on the British Household Panel Survey (BHPS) which ran from 1991-2009 and had around 10,000 households in it. Understanding Society started in 2009 and interviewed around 40,000 households, including around 8,000 of the orginal BHPS households.The USOC examines how life in the UK is changing and what stays the same over many years and includes questions on various topics including social, economical and behavioral factors. Interviews are held with each member of the household in order to examine how different generations experience life in the UK.
More information at: https://www.understandingsociety.ac.uk/about/about-the-study
Country/Countries: United Kingdom
Waves: 2008, 2013, 2014
Data collection period: Waves 1-11, 2008-2018
Dataset(s) version number/name: Understanding Society: Innovation Panel
Data access: https://www.understandingsociety.ac.uk/documentation/access-data
Age distribution:
Risk-taking propensity density:
General
4. Multi-level model results
4.1. General risk-taking
Intercept only model
Models results:
Fixed effect model
Models results:
Linear model
Models results:
Plot age trajectory:
Linear with gender model
Models results:
Plot age trajectory:
Linear with gender interaction model
Models results:
Plot age trajectory:
Quadratic model
Models results:
Plot age trajectory:
Quadratic with gender model
Models results:
Plot age trajectory:
4.2. Financial risk-taking
Intercept only model
Models results:
Fixed effect model
Models results:
Linear model
Models results:
Plot age trajectory:
Linear with gender model
Models results:
Plot age trajectory:
Linear with gender interaction model
Models results:
Plot age trajectory:
Quadratic model
Models results:
Plot age trajectory:
Quadratic with gender model
Models results:
Plot age trajectory:
4.3. Driving risk-taking
Intercept only model
Models results:
Fixed effect model
Models results:
Linear model
Models results:
Plot age trajectory:
Linear with gender model
Models results:
Plot age trajectory:
Linear with gender interaction model
Models results:
Plot age trajectory:
4.4. Recreational risk-taking
Intercept only model
Models results:
Fixed effect model
Models results:
Linear model
Models results:
Plot age trajectory:
Linear with gender model
Models results:
Plot age trajectory:
Linear with gender interaction model
Models results:
Plot age trajectory:
4.5. Occupational risk-taking
Intercept only model
Models results:
Fixed effect model
Models results:
Linear model
Models results:
Plot age trajectory:
Linear with gender model
Models results:
Plot age trajectory:
Linear with gender interaction model
Models results:
Plot age trajectory:
4.6. Health risk-taking
Intercept only model
Models results:
Fixed effect model
Models results:
Linear model
Models results:
Plot age trajectory:
Linear with gender model
Models results:
Plot age trajectory:
Linear with gender interaction model
Models results:
Plot age trajectory:
5. Meta-analysis results
5.1. General risk-taking
Intercept only model
Meta analysis:
ICC’s results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.9760 -7.9520 -3.9520 -4.3685 0.0480
##
## tau^2 (estimated amount of total heterogeneity): 0.0155 (SE = 0.0090)
## tau (square root of estimated tau^2 value): 0.1246
## I^2 (total heterogeneity / total variability): 99.82%
## H^2 (total variability / sampling variability): 565.14
##
## Test for Heterogeneity:
## Q(df = 6) = 4528.3812, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4330 0.0472 9.1640 <.0001 0.3404 0.5256 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
ICC’s results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.9116 -5.8233 2.1767 -0.2781 42.1767
##
## tau^2 (estimated amount of residual heterogeneity): 0.0136 (SE = 0.0097)
## tau (square root of estimated tau^2 value): 0.1166
## I^2 (residual heterogeneity / unaccounted variability): 99.70%
## H^2 (unaccounted variability / sampling variability): 329.06
## R^2 (amount of heterogeneity accounted for): 12.37%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 1568.0826, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 2.8327, p-val = 0.2426
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3249 0.0826 3.9344 <.0001 0.1630 0.4867 ***
## continentEurope 0.1798 0.1068 1.6829 0.0924 -0.0296 0.3892 .
## continentNorth America 0.1097 0.1168 0.9388 0.3478 -0.1193 0.3387
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
ICC’s results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6029 -7.2058 -1.2058 -2.3775 22.7942
##
## tau^2 (estimated amount of residual heterogeneity): 0.0138 (SE = 0.0088)
## tau (square root of estimated tau^2 value): 0.1176
## I^2 (residual heterogeneity / unaccounted variability): 99.80%
## H^2 (unaccounted variability / sampling variability): 489.21
## R^2 (amount of heterogeneity accounted for): 10.98%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 4199.8167, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.7372, p-val = 0.1875
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0245 0.3131 0.0781 0.9377 -0.5893 0.6382
## mean.age 0.0080 0.0060 1.3180 0.1875 -0.0039 0.0198
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
ICC’s results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.9760 -7.9520 -3.9520 -4.3685 0.0480
##
## tau^2 (estimated amount of total heterogeneity): 0.0155 (SE = 0.0090)
## tau (square root of estimated tau^2 value): 0.1246
## I^2 (total heterogeneity / total variability): 99.82%
## H^2 (total variability / sampling variability): 565.14
##
## Test for Heterogeneity:
## Q(df = 6) = 4528.3812, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4330 0.0472 9.1640 <.0001 0.3404 0.5256 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fixed effect model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.5846 -23.1692 -19.1692 -19.5857 -15.1692
##
## tau^2 (estimated amount of total heterogeneity): 0.0010 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0321
## I^2 (total heterogeneity / total variability): 97.81%
## H^2 (total variability / sampling variability): 45.72
##
## Test for Heterogeneity:
## Q(df = 6) = 209.9416, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0767 0.0126 -6.1045 <.0001 -0.1014 -0.0521 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.4638 -14.9277 -6.9277 -9.3825 33.0723
##
## tau^2 (estimated amount of residual heterogeneity): 0.0012 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0349
## I^2 (residual heterogeneity / unaccounted variability): 96.97%
## H^2 (unaccounted variability / sampling variability): 32.99
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 109.9605, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.4546, p-val = 0.4832
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0837 0.0253 -3.3138 0.0009 -0.1332 -0.0342
## continentEurope -0.0066 0.0330 -0.1998 0.8416 -0.0712 0.0580
## continentNorth America 0.0316 0.0356 0.8892 0.3739 -0.0381 0.1013
##
## intrcpt ***
## continentEurope
## continentNorth America
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.3909 -18.7818 -12.7818 -13.9535 11.2182
##
## tau^2 (estimated amount of residual heterogeneity): 0.0012 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0340
## I^2 (residual heterogeneity / unaccounted variability): 98.08%
## H^2 (unaccounted variability / sampling variability): 51.98
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 183.1491, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5411, p-val = 0.4620
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1436 0.0916 -1.5681 0.1169 -0.3231 0.0359
## mean.age 0.0013 0.0018 0.7356 0.4620 -0.0022 0.0048
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.5846 -23.1692 -19.1692 -19.5857 -15.1692
##
## tau^2 (estimated amount of total heterogeneity): 0.0010 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0321
## I^2 (total heterogeneity / total variability): 97.81%
## H^2 (total variability / sampling variability): 45.72
##
## Test for Heterogeneity:
## Q(df = 6) = 209.9416, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0767 0.0126 -6.1045 <.0001 -0.1014 -0.0521 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.5819 -23.1638 -19.1638 -19.5803 -15.1638
##
## tau^2 (estimated amount of total heterogeneity): 0.0010 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0321
## I^2 (total heterogeneity / total variability): 97.75%
## H^2 (total variability / sampling variability): 44.45
##
## Test for Heterogeneity:
## Q(df = 6) = 192.8175, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0767 0.0126 -6.1043 <.0001 -0.1013 -0.0521 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.4614 -14.9228 -6.9228 -9.3776 33.0772
##
## tau^2 (estimated amount of residual heterogeneity): 0.0012 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0349
## I^2 (residual heterogeneity / unaccounted variability): 96.90%
## H^2 (unaccounted variability / sampling variability): 32.30
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 104.0509, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.4584, p-val = 0.4823
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0843 0.0252 -3.3417 0.0008 -0.1337 -0.0349
## continentEurope -0.0055 0.0329 -0.1680 0.8666 -0.0701 0.0590
## continentNorth America 0.0324 0.0355 0.9119 0.3618 -0.0372 0.1020
##
## intrcpt ***
## continentEurope
## continentNorth America
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.4042 -18.8085 -12.8085 -13.9802 11.1915
##
## tau^2 (estimated amount of residual heterogeneity): 0.0011 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0338
## I^2 (residual heterogeneity / unaccounted variability): 98.00%
## H^2 (unaccounted variability / sampling variability): 50.05
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 165.5061, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5782, p-val = 0.4470
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1455 0.0912 -1.5957 0.1106 -0.3242 0.0332
## mean.age 0.0013 0.0018 0.7604 0.4470 -0.0021 0.0048
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.5819 -23.1638 -19.1638 -19.5803 -15.1638
##
## tau^2 (estimated amount of total heterogeneity): 0.0010 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0321
## I^2 (total heterogeneity / total variability): 97.75%
## H^2 (total variability / sampling variability): 44.45
##
## Test for Heterogeneity:
## Q(df = 6) = 192.8175, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0767 0.0126 -6.1043 <.0001 -0.1013 -0.0521 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.2626 -22.5253 -18.5253 -18.9418 -14.5253
##
## tau^2 (estimated amount of total heterogeneity): 0.0012 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0342
## I^2 (total heterogeneity / total variability): 98.07%
## H^2 (total variability / sampling variability): 51.88
##
## Test for Heterogeneity:
## Q(df = 6) = 205.7391, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0775 0.0134 -5.8080 <.0001 -0.1037 -0.0514 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.6895 -9.3790 -5.3790 -5.7955 -1.3790
##
## tau^2 (estimated amount of total heterogeneity): 0.0118 (SE = 0.0072)
## tau (square root of estimated tau^2 value): 0.1086
## I^2 (total heterogeneity / total variability): 98.21%
## H^2 (total variability / sampling variability): 55.78
##
## Test for Heterogeneity:
## Q(df = 6) = 284.3511, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2392 0.0424 -5.6470 <.0001 -0.3222 -0.1562 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.2889 -14.5777 -6.5777 -9.0325 33.4223
##
## tau^2 (estimated amount of residual heterogeneity): 0.0013 (SE = 0.0010)
## tau (square root of estimated tau^2 value): 0.0367
## I^2 (residual heterogeneity / unaccounted variability): 97.23%
## H^2 (unaccounted variability / sampling variability): 36.10
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 107.9715, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.5427, p-val = 0.4624
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0868 0.0265 -3.2810 0.0010 -0.1387 -0.0350 **
## continentEurope -0.0040 0.0345 -0.1168 0.9070 -0.0717 0.0636
## continentNorth America 0.0363 0.0373 0.9738 0.3302 -0.0368 0.1094
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.1904 -6.3808 1.6192 -0.8356 41.6192
##
## tau^2 (estimated amount of residual heterogeneity): 0.0114 (SE = 0.0087)
## tau (square root of estimated tau^2 value): 0.1067
## I^2 (residual heterogeneity / unaccounted variability): 96.98%
## H^2 (unaccounted variability / sampling variability): 33.08
## R^2 (amount of heterogeneity accounted for): 3.46%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 178.2441, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 2.1733, p-val = 0.3373
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3311 0.0768 -4.3112 <.0001 -0.4816 -0.1806
## continentEurope 0.1143 0.1008 1.1338 0.2569 -0.0833 0.3118
## continentNorth America 0.1521 0.1082 1.4057 0.1598 -0.0600 0.3642
##
## intrcpt ***
## continentEurope
## continentNorth America
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.0675 -18.1351 -12.1351 -13.3068 11.8649
##
## tau^2 (estimated amount of residual heterogeneity): 0.0013 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0367
## I^2 (residual heterogeneity / unaccounted variability): 98.35%
## H^2 (unaccounted variability / sampling variability): 60.49
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 185.4029, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4015, p-val = 0.5263
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1398 0.0988 -1.4141 0.1573 -0.3335 0.0540
## mean.age 0.0012 0.0019 0.6336 0.5263 -0.0025 0.0049
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6728 -7.3456 -1.3456 -2.5173 22.6544
##
## tau^2 (estimated amount of residual heterogeneity): 0.0130 (SE = 0.0088)
## tau (square root of estimated tau^2 value): 0.1139
## I^2 (residual heterogeneity / unaccounted variability): 98.19%
## H^2 (unaccounted variability / sampling variability): 55.24
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 220.1292, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4848, p-val = 0.4863
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4496 0.3054 -1.4723 0.1409 -1.0482 0.1489
## mean.age 0.0041 0.0059 0.6962 0.4863 -0.0074 0.0156
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.2626 -22.5253 -18.5253 -18.9418 -14.5253
##
## tau^2 (estimated amount of total heterogeneity): 0.0012 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0342
## I^2 (total heterogeneity / total variability): 98.07%
## H^2 (total variability / sampling variability): 51.88
##
## Test for Heterogeneity:
## Q(df = 6) = 205.7391, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0775 0.0134 -5.8080 <.0001 -0.1037 -0.0514 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.6895 -9.3790 -5.3790 -5.7955 -1.3790
##
## tau^2 (estimated amount of total heterogeneity): 0.0118 (SE = 0.0072)
## tau (square root of estimated tau^2 value): 0.1086
## I^2 (total heterogeneity / total variability): 98.21%
## H^2 (total variability / sampling variability): 55.78
##
## Test for Heterogeneity:
## Q(df = 6) = 284.3511, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2392 0.0424 -5.6470 <.0001 -0.3222 -0.1562 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender interaction model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.4637 -20.9274 -16.9274 -17.3438 -12.9274
##
## tau^2 (estimated amount of total heterogeneity): 0.0013 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0363
## I^2 (total heterogeneity / total variability): 96.41%
## H^2 (total variability / sampling variability): 27.87
##
## Test for Heterogeneity:
## Q(df = 6) = 92.7450, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0806 0.0145 -5.5610 <.0001 -0.1091 -0.0522 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.6721 -9.3442 -5.3442 -5.7607 -1.3442
##
## tau^2 (estimated amount of total heterogeneity): 0.0118 (SE = 0.0073)
## tau (square root of estimated tau^2 value): 0.1087
## I^2 (total heterogeneity / total variability): 97.61%
## H^2 (total variability / sampling variability): 41.77
##
## Test for Heterogeneity:
## Q(df = 6) = 257.8682, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2311 0.0425 -5.4373 <.0001 -0.3144 -0.1478 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 13.9104 -27.8209 -23.8209 -24.2373 -19.8209
##
## tau^2 (estimated amount of total heterogeneity): 0.0004 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0190
## I^2 (total heterogeneity / total variability): 79.78%
## H^2 (total variability / sampling variability): 4.94
##
## Test for Heterogeneity:
## Q(df = 6) = 32.0022, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0074 0.0088 0.8469 0.3971 -0.0098 0.0246
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 6.5136 -13.0272 -5.0272 -7.4820 34.9728
##
## tau^2 (estimated amount of residual heterogeneity): 0.0018 (SE = 0.0014)
## tau (square root of estimated tau^2 value): 0.0422
## I^2 (residual heterogeneity / unaccounted variability): 95.69%
## H^2 (unaccounted variability / sampling variability): 23.19
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 64.1273, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.0897, p-val = 0.5799
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0940 0.0308 -3.0520 0.0023 -0.1544 -0.0336 **
## continentEurope 0.0020 0.0404 0.0496 0.9604 -0.0771 0.0811
## continentNorth America 0.0391 0.0433 0.9028 0.3666 -0.0458 0.1241
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.0362 -6.0724 1.9276 -0.5272 41.9276
##
## tau^2 (estimated amount of residual heterogeneity): 0.0123 (SE = 0.0094)
## tau (square root of estimated tau^2 value): 0.1109
## I^2 (residual heterogeneity / unaccounted variability): 96.47%
## H^2 (unaccounted variability / sampling variability): 28.30
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 174.4541, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.7403, p-val = 0.4189
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3140 0.0798 -3.9350 <.0001 -0.4704 -0.1576
## continentEurope 0.0975 0.1046 0.9318 0.3515 -0.1075 0.3025
## continentNorth America 0.1455 0.1129 1.2890 0.1974 -0.0757 0.3667
##
## intrcpt ***
## continentEurope
## continentNorth America
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0194
## I^2 (residual heterogeneity / unaccounted variability): 71.00%
## H^2 (unaccounted variability / sampling variability): 3.45
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 11.7950, p-val = 0.0189
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.9695, p-val = 0.6158
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0188 0.0166 1.1344 0.2566 -0.0137 0.0513
## continentEurope -0.0212 0.0218 -0.9736 0.3302 -0.0640 0.0215
## continentNorth America -0.0095 0.0230 -0.4111 0.6810 -0.0546 0.0357
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 8.7140 -17.4279 -11.4279 -12.5996 12.5721
##
## tau^2 (estimated amount of residual heterogeneity): 0.0012 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0353
## I^2 (residual heterogeneity / unaccounted variability): 96.30%
## H^2 (unaccounted variability / sampling variability): 27.05
## R^2 (amount of heterogeneity accounted for): 5.43%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 62.3278, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.2977, p-val = 0.2546
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1894 0.0966 -1.9597 0.0500 -0.3787 0.0000 .
## mean.age 0.0021 0.0019 1.1392 0.2546 -0.0015 0.0058
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6387 -7.2773 -1.2773 -2.4490 22.7227
##
## tau^2 (estimated amount of residual heterogeneity): 0.0131 (SE = 0.0089)
## tau (square root of estimated tau^2 value): 0.1145
## I^2 (residual heterogeneity / unaccounted variability): 97.90%
## H^2 (unaccounted variability / sampling variability): 47.63
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 229.6337, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4255, p-val = 0.5142
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4311 0.3096 -1.3921 0.1639 -1.0379 0.1758
## mean.age 0.0039 0.0060 0.6523 0.5142 -0.0078 0.0156
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0149
## I^2 (residual heterogeneity / unaccounted variability): 71.15%
## H^2 (unaccounted variability / sampling variability): 3.47
## R^2 (amount of heterogeneity accounted for): 38.45%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 20.0815, p-val = 0.0012
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.3001, p-val = 0.0693
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0942 0.0483 1.9512 0.0510 -0.0004 0.1888 .
## mean.age -0.0017 0.0009 -1.8166 0.0693 -0.0036 0.0001 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.4637 -20.9274 -16.9274 -17.3438 -12.9274
##
## tau^2 (estimated amount of total heterogeneity): 0.0013 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0363
## I^2 (total heterogeneity / total variability): 96.41%
## H^2 (total variability / sampling variability): 27.87
##
## Test for Heterogeneity:
## Q(df = 6) = 92.7450, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0806 0.0145 -5.5610 <.0001 -0.1091 -0.0522 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.6721 -9.3442 -5.3442 -5.7607 -1.3442
##
## tau^2 (estimated amount of total heterogeneity): 0.0118 (SE = 0.0073)
## tau (square root of estimated tau^2 value): 0.1087
## I^2 (total heterogeneity / total variability): 97.61%
## H^2 (total variability / sampling variability): 41.77
##
## Test for Heterogeneity:
## Q(df = 6) = 257.8682, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2311 0.0425 -5.4373 <.0001 -0.3144 -0.1478 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 13.9104 -27.8209 -23.8209 -24.2373 -19.8209
##
## tau^2 (estimated amount of total heterogeneity): 0.0004 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0190
## I^2 (total heterogeneity / total variability): 79.78%
## H^2 (total variability / sampling variability): 4.94
##
## Test for Heterogeneity:
## Q(df = 6) = 32.0022, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0074 0.0088 0.8469 0.3971 -0.0098 0.0246
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Quadratic model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.2079 -14.4159 -10.4159 -12.2186 1.5841
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0213
## I^2 (total heterogeneity / total variability): 96.04%
## H^2 (total variability / sampling variability): 25.25
##
## Test for Heterogeneity:
## Q(df = 3) = 63.5945, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0838 0.0110 -7.5977 <.0001 -0.1054 -0.0622 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 8.6378 -17.2756 -13.2756 -15.0784 -1.2756
##
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0133
## I^2 (total heterogeneity / total variability): 97.33%
## H^2 (total variability / sampling variability): 37.51
##
## Test for Heterogeneity:
## Q(df = 3) = 174.4010, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0017 0.0069 -0.2438 0.8074 -0.0152 0.0118
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.8784 -7.7569 0.2431 -7.7569 40.2431
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.1527, p-val = 0.6959
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 63.4417, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0949 0.0039 -24.5103 <.0001 -0.1025 -0.0873
## continentEurope -0.0019 0.0044 -0.4375 0.6617 -0.0104 0.0066
## continentNorth America 0.0434 0.0066 6.5634 <.0001 0.0305 0.0564
##
## intrcpt ***
## continentEurope
## continentNorth America ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.5526 -5.1053 2.8947 -5.1053 42.8947
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0182
## I^2 (residual heterogeneity / unaccounted variability): 93.04%
## H^2 (unaccounted variability / sampling variability): 14.38
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.3760, p-val = 0.0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.5824, p-val = 0.7474
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0076 0.0133 -0.5729 0.5667 -0.0337 0.0185
## continentEurope 0.0172 0.0225 0.7630 0.4454 -0.0270 0.0614
## continentNorth America 0.0063 0.0227 0.2778 0.7812 -0.0382 0.0508
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.4494 -8.8988 -2.8988 -6.8194 21.1012
##
## tau^2 (estimated amount of residual heterogeneity): 0.0007 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0256
## I^2 (residual heterogeneity / unaccounted variability): 96.70%
## H^2 (unaccounted variability / sampling variability): 30.33
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 2) = 62.8037, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0761, p-val = 0.7826
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1272 0.1578 -0.8061 0.4202 -0.4364 0.1820
## mean.age 0.0009 0.0033 0.2759 0.7826 -0.0056 0.0075
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 6.8996 -13.7992 -7.7992 -11.7198 16.2008
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0072
## I^2 (residual heterogeneity / unaccounted variability): 88.44%
## H^2 (unaccounted variability / sampling variability): 8.65
## R^2 (amount of heterogeneity accounted for): 70.82%
##
## Test for Residual Heterogeneity:
## QE(df = 2) = 20.5141, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 7.3444, p-val = 0.0067
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1333 0.0486 -2.7438 0.0061 -0.2285 -0.0381 **
## mean.age 0.0028 0.0010 2.7101 0.0067 0.0008 0.0048 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.2079 -14.4159 -10.4159 -12.2186 1.5841
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0213
## I^2 (total heterogeneity / total variability): 96.04%
## H^2 (total variability / sampling variability): 25.25
##
## Test for Heterogeneity:
## Q(df = 3) = 63.5945, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0838 0.0110 -7.5977 <.0001 -0.1054 -0.0622 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 8.6378 -17.2756 -13.2756 -15.0784 -1.2756
##
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0133
## I^2 (total heterogeneity / total variability): 97.33%
## H^2 (total variability / sampling variability): 37.51
##
## Test for Heterogeneity:
## Q(df = 3) = 174.4010, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0017 0.0069 -0.2438 0.8074 -0.0152 0.0118
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Quadratic with gender model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 6.9518 -13.9036 -9.9036 -11.7064 2.0964
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0231
## I^2 (total heterogeneity / total variability): 96.71%
## H^2 (total variability / sampling variability): 30.38
##
## Test for Heterogeneity:
## Q(df = 3) = 69.7329, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0842 0.0119 -7.0709 <.0001 -0.1075 -0.0608 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 8.8141 -17.6282 -13.6282 -15.4310 -1.6282
##
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0125
## I^2 (total heterogeneity / total variability): 97.09%
## H^2 (total variability / sampling variability): 34.37
##
## Test for Heterogeneity:
## Q(df = 3) = 163.4501, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0019 0.0065 -0.2928 0.7697 -0.0147 0.0109
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.9033 -5.8067 -1.8067 -3.6095 10.1933
##
## tau^2 (estimated amount of total heterogeneity): 0.0081 (SE = 0.0069)
## tau (square root of estimated tau^2 value): 0.0900
## I^2 (total heterogeneity / total variability): 97.85%
## H^2 (total variability / sampling variability): 46.60
##
## Test for Heterogeneity:
## Q(df = 3) = 94.2788, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2864 0.0458 -6.2497 <.0001 -0.3762 -0.1966 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.5250 -7.0500 0.9500 -7.0500 40.9500
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.8964, p-val = 0.3437
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 68.8365, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0926 0.0038 -24.3258 <.0001 -0.1000 -0.0851
## continentEurope -0.0039 0.0043 -0.9013 0.3674 -0.0122 0.0045
## continentNorth America 0.0434 0.0066 6.6023 <.0001 0.0305 0.0563
##
## intrcpt ***
## continentEurope
## continentNorth America ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.6318 -5.2637 2.7363 -5.2637 42.7363
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0167
## I^2 (residual heterogeneity / unaccounted variability): 92.13%
## H^2 (unaccounted variability / sampling variability): 12.70
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 12.7009, p-val = 0.0004
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.6519, p-val = 0.7218
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0078 0.0123 -0.6330 0.5267 -0.0319 0.0163
## continentEurope 0.0168 0.0208 0.8071 0.4196 -0.0239 0.0574
## continentNorth America 0.0063 0.0209 0.3018 0.7628 -0.0347 0.0473
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1327 -4.2654 3.7346 -4.2654 43.7346
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0012)
## tau (square root of estimated tau^2 value): 0.0202
## I^2 (residual heterogeneity / unaccounted variability): 49.47%
## H^2 (unaccounted variability / sampling variability): 1.98
## R^2 (amount of heterogeneity accounted for): 94.98%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.9790, p-val = 0.1595
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 34.2919, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3235 0.0191 -16.9022 <.0001 -0.3610 -0.2860
## continentEurope -0.0117 0.0287 -0.4071 0.6839 -0.0679 0.0446
## continentNorth America 0.1730 0.0331 5.2319 <.0001 0.1082 0.2378
##
## intrcpt ***
## continentEurope
## continentNorth America ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.2358 -8.4715 -2.4715 -6.3921 21.5285
##
## tau^2 (estimated amount of residual heterogeneity): 0.0008 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0285
## I^2 (residual heterogeneity / unaccounted variability): 97.38%
## H^2 (unaccounted variability / sampling variability): 38.21
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 2) = 69.6462, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0039, p-val = 0.9504
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0734 0.1748 -0.4200 0.6745 -0.4161 0.2692
## mean.age -0.0002 0.0037 -0.0622 0.9504 -0.0075 0.0070
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 6.9450 -13.8899 -7.8899 -11.8105 16.1101
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0070
## I^2 (residual heterogeneity / unaccounted variability): 88.04%
## H^2 (unaccounted variability / sampling variability): 8.36
## R^2 (amount of heterogeneity accounted for): 68.89%
##
## Test for Residual Heterogeneity:
## QE(df = 2) = 19.5855, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 6.7660, p-val = 0.0093
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1250 0.0473 -2.6398 0.0083 -0.2178 -0.0322 **
## mean.age 0.0026 0.0010 2.6012 0.0093 0.0006 0.0046 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5340 -3.0679 2.9321 -0.9885 26.9321
##
## tau^2 (estimated amount of residual heterogeneity): 0.0123 (SE = 0.0126)
## tau (square root of estimated tau^2 value): 0.1109
## I^2 (residual heterogeneity / unaccounted variability): 98.26%
## H^2 (unaccounted variability / sampling variability): 57.60
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 2) = 93.9531, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0085, p-val = 0.9264
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2245 0.6720 -0.3341 0.7383 -1.5416 1.0925
## mean.age -0.0013 0.0142 -0.0924 0.9264 -0.0291 0.0265
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 6.9518 -13.9036 -9.9036 -11.7064 2.0964
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0231
## I^2 (total heterogeneity / total variability): 96.71%
## H^2 (total variability / sampling variability): 30.38
##
## Test for Heterogeneity:
## Q(df = 3) = 69.7329, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0842 0.0119 -7.0709 <.0001 -0.1075 -0.0608 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 8.8141 -17.6282 -13.6282 -15.4310 -1.6282
##
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0125
## I^2 (total heterogeneity / total variability): 97.09%
## H^2 (total variability / sampling variability): 34.37
##
## Test for Heterogeneity:
## Q(df = 3) = 163.4501, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0019 0.0065 -0.2928 0.7697 -0.0147 0.0109
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.9033 -5.8067 -1.8067 -3.6095 10.1933
##
## tau^2 (estimated amount of total heterogeneity): 0.0081 (SE = 0.0069)
## tau (square root of estimated tau^2 value): 0.0900
## I^2 (total heterogeneity / total variability): 97.85%
## H^2 (total variability / sampling variability): 46.60
##
## Test for Heterogeneity:
## Q(df = 3) = 94.2788, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2864 0.0458 -6.2497 <.0001 -0.3762 -0.1966 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.2. Financial risk-taking
Intercept only model
Meta analysis:
ICC’s results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 14.3823 -28.7646 -24.7646 -22.8757 -24.0146
##
## tau^2 (estimated amount of total heterogeneity): 0.0123 (SE = 0.0042)
## tau (square root of estimated tau^2 value): 0.1108
## I^2 (total heterogeneity / total variability): 99.39%
## H^2 (total variability / sampling variability): 162.64
##
## Test for Heterogeneity:
## Q(df = 19) = 2085.1811, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.3615 0.0253 14.2635 <.0001 0.3118 0.4111 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
ICC’s results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.9747 -23.9493 -13.9493 -10.0864 -7.9493
##
## tau^2 (estimated amount of residual heterogeneity): 0.0125 (SE = 0.0046)
## tau (square root of estimated tau^2 value): 0.1119
## I^2 (residual heterogeneity / unaccounted variability): 98.94%
## H^2 (unaccounted variability / sampling variability): 93.92
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 2005.5145, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.6862, p-val = 0.4426
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2601 0.1190 2.1869 0.0288 0.0270 0.4933 *
## continentEurope 0.0935 0.1222 0.7652 0.4442 -0.1459 0.3329
## continentNorth America 0.2033 0.1636 1.2426 0.2140 -0.1173 0.5238
## continentOceania 0.2163 0.1633 1.3245 0.1853 -0.1038 0.5364
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
ICC’s results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 17.3289 -34.6577 -28.6577 -25.9866 -26.9435
##
## tau^2 (estimated amount of residual heterogeneity): 0.0080 (SE = 0.0029)
## tau (square root of estimated tau^2 value): 0.0896
## I^2 (residual heterogeneity / unaccounted variability): 99.03%
## H^2 (unaccounted variability / sampling variability): 102.64
## R^2 (amount of heterogeneity accounted for): 34.65%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 1806.2165, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 10.6074, p-val = 0.0011
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.9826 0.1914 5.1331 <.0001 0.6074 1.3577 ***
## mean.age -0.0101 0.0031 -3.2569 0.0011 -0.0161 -0.0040 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
ICC’s results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 16.3238 -32.6476 -26.6476 -23.9765 -24.9333
##
## tau^2 (estimated amount of residual heterogeneity): 0.0090 (SE = 0.0032)
## tau (square root of estimated tau^2 value): 0.0951
## I^2 (residual heterogeneity / unaccounted variability): 98.87%
## H^2 (unaccounted variability / sampling variability): 88.12
## R^2 (amount of heterogeneity accounted for): 26.40%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 2063.7542, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 7.5989, p-val = 0.0058
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2352 0.0511 4.6057 <.0001 0.1351 0.3353 ***
## scale1 0.0235 0.0085 2.7566 0.0058 0.0068 0.0403 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fixed effect model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 30.9607 -61.9214 -57.9214 -56.0326 -57.1714
##
## tau^2 (estimated amount of total heterogeneity): 0.0019 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0437
## I^2 (total heterogeneity / total variability): 97.04%
## H^2 (total variability / sampling variability): 33.78
##
## Test for Heterogeneity:
## Q(df = 19) = 409.2674, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1127 0.0104 -10.8505 <.0001 -0.1331 -0.0924 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 25.7176 -51.4353 -41.4353 -37.5723 -35.4353
##
## tau^2 (estimated amount of residual heterogeneity): 0.0020 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0445
## I^2 (residual heterogeneity / unaccounted variability): 95.68%
## H^2 (unaccounted variability / sampling variability): 23.14
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 175.6741, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.1424, p-val = 0.5434
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1378 0.0558 -2.4676 0.0136 -0.2472 -0.0283 *
## continentEurope 0.0222 0.0570 0.3890 0.6973 -0.0895 0.1339
## continentNorth America 0.0219 0.0727 0.3009 0.7635 -0.1207 0.1644
## continentOceania 0.0861 0.0715 1.2046 0.2284 -0.0540 0.2261
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 31.7002 -63.4005 -57.4005 -54.7293 -55.6862
##
## tau^2 (estimated amount of residual heterogeneity): 0.0013 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0366
## I^2 (residual heterogeneity / unaccounted variability): 95.51%
## H^2 (unaccounted variability / sampling variability): 22.27
## R^2 (amount of heterogeneity accounted for): 29.85%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 216.5698, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 7.0971, p-val = 0.0077
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0988 0.0796 1.2410 0.2146 -0.0572 0.2548
## mean.age -0.0034 0.0013 -2.6640 0.0077 -0.0060 -0.0009 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 29.4937 -58.9874 -52.9874 -50.3163 -51.2731
##
## tau^2 (estimated amount of residual heterogeneity): 0.0019 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0433
## I^2 (residual heterogeneity / unaccounted variability): 95.90%
## H^2 (unaccounted variability / sampling variability): 24.39
## R^2 (amount of heterogeneity accounted for): 1.81%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 367.4758, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.4274, p-val = 0.2322
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1386 0.0240 -5.7736 <.0001 -0.1857 -0.0916 ***
## scale1 0.0048 0.0040 1.1947 0.2322 -0.0030 0.0126
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 30.6677 -61.3353 -57.3353 -55.4464 -56.5853
##
## tau^2 (estimated amount of total heterogeneity): 0.0020 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0447
## I^2 (total heterogeneity / total variability): 96.85%
## H^2 (total variability / sampling variability): 31.77
##
## Test for Heterogeneity:
## Q(df = 19) = 377.9896, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1129 0.0106 -10.6668 <.0001 -0.1336 -0.0921 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 25.4312 -50.8625 -40.8625 -36.9995 -34.8625
##
## tau^2 (estimated amount of residual heterogeneity): 0.0021 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0458
## I^2 (residual heterogeneity / unaccounted variability): 95.74%
## H^2 (unaccounted variability / sampling variability): 23.46
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 187.0364, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.0462, p-val = 0.5629
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1420 0.0561 -2.5307 0.0114 -0.2519 -0.0320 *
## continentEurope 0.0266 0.0573 0.4636 0.6430 -0.0858 0.1389
## continentNorth America 0.0255 0.0737 0.3461 0.7293 -0.1189 0.1699
## continentOceania 0.0893 0.0724 1.2326 0.2177 -0.0527 0.2313
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 31.0126 -62.0253 -56.0253 -53.3541 -54.3110
##
## tau^2 (estimated amount of residual heterogeneity): 0.0015 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0389
## I^2 (residual heterogeneity / unaccounted variability): 95.56%
## H^2 (unaccounted variability / sampling variability): 22.55
## R^2 (amount of heterogeneity accounted for): 24.32%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 223.7629, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 5.8572, p-val = 0.0155
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0906 0.0844 1.0737 0.2830 -0.0748 0.2559
## mean.age -0.0033 0.0014 -2.4202 0.0155 -0.0060 -0.0006 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 29.1185 -58.2369 -52.2369 -49.5658 -50.5227
##
## tau^2 (estimated amount of residual heterogeneity): 0.0020 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0446
## I^2 (residual heterogeneity / unaccounted variability): 95.83%
## H^2 (unaccounted variability / sampling variability): 23.96
## R^2 (amount of heterogeneity accounted for): 0.57%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 353.3467, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.2069, p-val = 0.2720
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1373 0.0246 -5.5805 <.0001 -0.1855 -0.0891 ***
## scale1 0.0045 0.0041 1.0986 0.2720 -0.0035 0.0125
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 31.3751 -62.7501 -58.7501 -56.8613 -58.0001
##
## tau^2 (estimated amount of total heterogeneity): 0.0018 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0429
## I^2 (total heterogeneity / total variability): 96.68%
## H^2 (total variability / sampling variability): 30.17
##
## Test for Heterogeneity:
## Q(df = 19) = 376.7695, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1139 0.0102 -11.1774 <.0001 -0.1338 -0.0939 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 14.4907 -28.9813 -24.9813 -23.0925 -24.2313
##
## tau^2 (estimated amount of total heterogeneity): 0.0120 (SE = 0.0042)
## tau (square root of estimated tau^2 value): 0.1095
## I^2 (total heterogeneity / total variability): 96.57%
## H^2 (total variability / sampling variability): 29.16
##
## Test for Heterogeneity:
## Q(df = 19) = 1017.8345, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2714 0.0255 -10.6541 <.0001 -0.3213 -0.2215 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 26.1776 -52.3552 -42.3552 -38.4923 -36.3552
##
## tau^2 (estimated amount of residual heterogeneity): 0.0019 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0435
## I^2 (residual heterogeneity / unaccounted variability): 95.43%
## H^2 (unaccounted variability / sampling variability): 21.90
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 170.2433, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.3924, p-val = 0.4951
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1475 0.0540 -2.7321 0.0063 -0.2533 -0.0417 **
## continentEurope 0.0316 0.0551 0.5738 0.5661 -0.0764 0.1396
## continentNorth America 0.0236 0.0706 0.3351 0.7375 -0.1147 0.1620
## continentOceania 0.0940 0.0693 1.3557 0.1752 -0.0419 0.2299
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 13.1073 -26.2146 -16.2146 -12.3516 -10.2146
##
## tau^2 (estimated amount of residual heterogeneity): 0.0105 (SE = 0.0040)
## tau (square root of estimated tau^2 value): 0.1024
## I^2 (residual heterogeneity / unaccounted variability): 95.07%
## H^2 (unaccounted variability / sampling variability): 20.27
## R^2 (amount of heterogeneity accounted for): 12.44%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 480.7958, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 5.2341, p-val = 0.1554
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2942 0.1160 -2.5357 0.0112 -0.5216 -0.0668 *
## continentEurope 0.0190 0.1189 0.1595 0.8733 -0.2140 0.2520
## continentNorth America -0.1007 0.1568 -0.6418 0.5210 -0.4080 0.2067
## continentOceania 0.2231 0.1549 1.4402 0.1498 -0.0805 0.5268
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 31.9259 -63.8517 -57.8517 -55.1806 -56.1374
##
## tau^2 (estimated amount of residual heterogeneity): 0.0014 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0368
## I^2 (residual heterogeneity / unaccounted variability): 95.22%
## H^2 (unaccounted variability / sampling variability): 20.92
## R^2 (amount of heterogeneity accounted for): 26.39%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 231.7036, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 6.4845, p-val = 0.0109
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0894 0.0801 1.1171 0.2640 -0.0675 0.2463
## mean.age -0.0033 0.0013 -2.5465 0.0109 -0.0059 -0.0008 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 13.4458 -26.8915 -20.8915 -18.2204 -19.1772
##
## tau^2 (estimated amount of residual heterogeneity): 0.0124 (SE = 0.0044)
## tau (square root of estimated tau^2 value): 0.1111
## I^2 (residual heterogeneity / unaccounted variability): 96.25%
## H^2 (unaccounted variability / sampling variability): 26.63
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 1013.7999, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4262, p-val = 0.5139
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4269 0.2395 -1.7825 0.0747 -0.8963 0.0425 .
## mean.age 0.0025 0.0039 0.6528 0.5139 -0.0051 0.0101
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 29.8900 -59.7801 -53.7801 -51.1090 -52.0658
##
## tau^2 (estimated amount of residual heterogeneity): 0.0018 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0426
## I^2 (residual heterogeneity / unaccounted variability): 95.56%
## H^2 (unaccounted variability / sampling variability): 22.51
## R^2 (amount of heterogeneity accounted for): 1.49%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 349.8997, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.4224, p-val = 0.2330
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1392 0.0236 -5.9041 <.0001 -0.1854 -0.0930 ***
## scale1 0.0047 0.0039 1.1927 0.2330 -0.0030 0.0123
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 15.1407 -30.2814 -24.2814 -21.6102 -22.5671
##
## tau^2 (estimated amount of residual heterogeneity): 0.0098 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0989
## I^2 (residual heterogeneity / unaccounted variability): 95.41%
## H^2 (unaccounted variability / sampling variability): 21.79
## R^2 (amount of heterogeneity accounted for): 18.43%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 480.0662, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.3693, p-val = 0.0366
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1683 0.0543 -3.1020 0.0019 -0.2747 -0.0620 **
## scale1 -0.0191 0.0091 -2.0903 0.0366 -0.0370 -0.0012 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender interaction model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 27.0743 -54.1486 -50.1486 -48.2597 -49.3986
##
## tau^2 (estimated amount of total heterogeneity): 0.0027 (SE = 0.0010)
## tau (square root of estimated tau^2 value): 0.0524
## I^2 (total heterogeneity / total variability): 95.52%
## H^2 (total variability / sampling variability): 22.34
##
## Test for Heterogeneity:
## Q(df = 19) = 284.5991, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1316 0.0127 -10.3280 <.0001 -0.1565 -0.1066 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.6177 -21.2353 -17.2353 -15.3465 -16.4853
##
## tau^2 (estimated amount of total heterogeneity): 0.0152 (SE = 0.0060)
## tau (square root of estimated tau^2 value): 0.1235
## I^2 (total heterogeneity / total variability): 95.07%
## H^2 (total variability / sampling variability): 20.27
##
## Test for Heterogeneity:
## Q(df = 19) = 879.8015, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.3169 0.0306 -10.3417 <.0001 -0.3769 -0.2568 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 34.0779 -68.1558 -64.1558 -62.2669 -63.4058
##
## tau^2 (estimated amount of total heterogeneity): 0.0007 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0256
## I^2 (total heterogeneity / total variability): 73.20%
## H^2 (total variability / sampling variability): 3.73
##
## Test for Heterogeneity:
## Q(df = 19) = 57.0636, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0308 0.0080 3.8333 0.0001 0.0151 0.0466 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 22.8553 -45.7106 -35.7106 -31.8477 -29.7106
##
## tau^2 (estimated amount of residual heterogeneity): 0.0027 (SE = 0.0011)
## tau (square root of estimated tau^2 value): 0.0520
## I^2 (residual heterogeneity / unaccounted variability): 93.33%
## H^2 (unaccounted variability / sampling variability): 15.00
## R^2 (amount of heterogeneity accounted for): 1.49%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 111.7471, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 3.0411, p-val = 0.3853
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1796 0.0701 -2.5619 0.0104 -0.3170 -0.0422 *
## continentEurope 0.0428 0.0714 0.5997 0.5487 -0.0972 0.1828
## continentNorth America 0.0806 0.0899 0.8968 0.3698 -0.0955 0.2567
## continentOceania 0.1234 0.0873 1.4124 0.1578 -0.0478 0.2946
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.9628 -19.9255 -9.9255 -6.0626 -3.9255
##
## tau^2 (estimated amount of residual heterogeneity): 0.0123 (SE = 0.0054)
## tau (square root of estimated tau^2 value): 0.1109
## I^2 (residual heterogeneity / unaccounted variability): 90.92%
## H^2 (unaccounted variability / sampling variability): 11.01
## R^2 (amount of heterogeneity accounted for): 19.37%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 118.7507, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 5.4797, p-val = 0.1399
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4147 0.1792 -2.3135 0.0207 -0.7660 -0.0634 *
## continentEurope 0.0839 0.1818 0.4618 0.6442 -0.2723 0.4402
## continentNorth America 0.0998 0.2190 0.4557 0.6486 -0.3294 0.5290
## continentOceania 0.3456 0.2109 1.6389 0.1012 -0.0677 0.7590
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0206
## I^2 (residual heterogeneity / unaccounted variability): 53.62%
## H^2 (unaccounted variability / sampling variability): 2.16
## R^2 (amount of heterogeneity accounted for): 35.51%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 32.6006, p-val = 0.0083
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 6.5131, p-val = 0.0891
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0595 0.0674 0.8838 0.3768 -0.0725 0.1916
## continentEurope -0.0229 0.0678 -0.3378 0.7355 -0.1558 0.1100
## continentNorth America -0.1001 0.0755 -1.3244 0.1854 -0.2481 0.0480
## continentOceania -0.0538 0.0706 -0.7620 0.4461 -0.1921 0.0845
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 27.5602 -55.1204 -49.1204 -46.4493 -47.4061
##
## tau^2 (estimated amount of residual heterogeneity): 0.0021 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0454
## I^2 (residual heterogeneity / unaccounted variability): 93.74%
## H^2 (unaccounted variability / sampling variability): 15.97
## R^2 (amount of heterogeneity accounted for): 24.99%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 169.0369, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 5.7560, p-val = 0.0164
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1073 0.0997 1.0755 0.2822 -0.0882 0.3028
## mean.age -0.0039 0.0016 -2.3992 0.0164 -0.0071 -0.0007 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.5544 -19.1088 -13.1088 -10.4377 -11.3946
##
## tau^2 (estimated amount of residual heterogeneity): 0.0164 (SE = 0.0066)
## tau (square root of estimated tau^2 value): 0.1279
## I^2 (residual heterogeneity / unaccounted variability): 95.13%
## H^2 (unaccounted variability / sampling variability): 20.54
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 836.3622, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0800, p-val = 0.7773
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2384 0.2807 -0.8493 0.3957 -0.7887 0.3118
## mean.age -0.0013 0.0046 -0.2828 0.7773 -0.0102 0.0077
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0007 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0269
## I^2 (residual heterogeneity / unaccounted variability): 73.51%
## H^2 (unaccounted variability / sampling variability): 3.77
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 48.3112, p-val = 0.0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4895, p-val = 0.4841
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0149 0.0662 -0.2259 0.8213 -0.1446 0.1147
## mean.age 0.0008 0.0011 0.6997 0.4841 -0.0014 0.0029
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 26.7318 -53.4636 -47.4636 -44.7924 -45.7493
##
## tau^2 (estimated amount of residual heterogeneity): 0.0024 (SE = 0.0010)
## tau (square root of estimated tau^2 value): 0.0491
## I^2 (residual heterogeneity / unaccounted variability): 93.13%
## H^2 (unaccounted variability / sampling variability): 14.56
## R^2 (amount of heterogeneity accounted for): 12.02%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 262.1502, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.4755, p-val = 0.0623
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1785 0.0281 -6.3478 <.0001 -0.2336 -0.1234 ***
## scale1 0.0086 0.0046 1.8643 0.0623 -0.0004 0.0177 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.6011 -19.2022 -13.2022 -10.5311 -11.4879
##
## tau^2 (estimated amount of residual heterogeneity): 0.0160 (SE = 0.0064)
## tau (square root of estimated tau^2 value): 0.1264
## I^2 (residual heterogeneity / unaccounted variability): 93.25%
## H^2 (unaccounted variability / sampling variability): 14.81
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 390.7190, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1251, p-val = 0.7236
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2938 0.0730 -4.0243 <.0001 -0.4369 -0.1507 ***
## scale1 -0.0043 0.0120 -0.3537 0.7236 -0.0279 0.0193
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 33.1445 -66.2889 -60.2889 -57.6178 -58.5747
##
## tau^2 (estimated amount of residual heterogeneity): 0.0007 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0261
## I^2 (residual heterogeneity / unaccounted variability): 67.13%
## H^2 (unaccounted variability / sampling variability): 3.04
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 56.6324, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.0470, p-val = 0.0809
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0611 0.0191 3.1934 0.0014 0.0236 0.0986 **
## scale1 -0.0053 0.0030 -1.7456 0.0809 -0.0112 0.0006 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Quadratic model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 13.6420 -27.2840 -23.2840 -22.0059 -22.1931
##
## tau^2 (estimated amount of total heterogeneity): 0.0046 (SE = 0.0025)
## tau (square root of estimated tau^2 value): 0.0681
## I^2 (total heterogeneity / total variability): 96.88%
## H^2 (total variability / sampling variability): 32.02
##
## Test for Heterogeneity:
## Q(df = 14) = 87.9472, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1173 0.0216 -5.4375 <.0001 -0.1595 -0.0750 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Random-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 34.8287 -69.6575 -65.6575 -64.3794 -64.5666
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0161
## I^2 (total heterogeneity / total variability): 89.72%
## H^2 (total variability / sampling variability): 9.73
##
## Test for Heterogeneity:
## Q(df = 14) = 278.0111, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0027 0.0053 0.5172 0.6050 -0.0076 0.0131
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 12.2865 -24.5731 -18.5731 -16.8782 -15.9064
##
## tau^2 (estimated amount of residual heterogeneity): 0.0050 (SE = 0.0029)
## tau (square root of estimated tau^2 value): 0.0709
## I^2 (residual heterogeneity / unaccounted variability): 91.94%
## H^2 (unaccounted variability / sampling variability): 12.41
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 60.8668, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7266, p-val = 0.3940
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0605 0.0709 -0.8533 0.3935 -0.1994 0.0785
## continentEurope -0.0636 0.0747 -0.8524 0.3940 -0.2100 0.0827
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 32.9785 -65.9569 -59.9569 -58.2621 -57.2903
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0135
## I^2 (residual heterogeneity / unaccounted variability): 70.53%
## H^2 (unaccounted variability / sampling variability): 3.39
## R^2 (amount of heterogeneity accounted for): 30.21%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 32.0001, p-val = 0.0024
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.4628, p-val = 0.0628
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0209 0.0135 -1.5504 0.1211 -0.0474 0.0055
## continentEurope 0.0268 0.0144 1.8609 0.0628 -0.0014 0.0550 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 13.2225 -26.4450 -20.4450 -18.7502 -17.7783
##
## tau^2 (estimated amount of residual heterogeneity): 0.0036 (SE = 0.0023)
## tau (square root of estimated tau^2 value): 0.0601
## I^2 (residual heterogeneity / unaccounted variability): 95.36%
## H^2 (unaccounted variability / sampling variability): 21.55
## R^2 (amount of heterogeneity accounted for): 22.04%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 61.1876, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.9137, p-val = 0.0878
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1815 0.1750 1.0374 0.2995 -0.1614 0.5245
## mean.age -0.0049 0.0029 -1.7070 0.0878 -0.0105 0.0007 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 31.7819 -63.5638 -57.5638 -55.8690 -54.8972
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0170
## I^2 (residual heterogeneity / unaccounted variability): 89.41%
## H^2 (unaccounted variability / sampling variability): 9.45
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 220.4669, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0887, p-val = 0.7658
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0119 0.0495 -0.2401 0.8102 -0.1090 0.0852
## mean.age 0.0002 0.0008 0.2978 0.7658 -0.0013 0.0018
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 12.4027 -24.8054 -18.8054 -17.1106 -16.1388
##
## tau^2 (estimated amount of residual heterogeneity): 0.0049 (SE = 0.0028)
## tau (square root of estimated tau^2 value): 0.0699
## I^2 (residual heterogeneity / unaccounted variability): 92.21%
## H^2 (unaccounted variability / sampling variability): 12.83
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 71.4622, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.9276, p-val = 0.3355
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1993 0.0875 -2.2765 0.0228 -0.3709 -0.0277 *
## scale1 0.0178 0.0185 0.9631 0.3355 -0.0184 0.0540
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 32.5749 -65.1497 -59.1497 -57.4549 -56.4831
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0146
## I^2 (residual heterogeneity / unaccounted variability): 76.70%
## H^2 (unaccounted variability / sampling variability): 4.29
## R^2 (amount of heterogeneity accounted for): 17.66%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 49.7671, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.0764, p-val = 0.1496
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0241 0.0193 -1.2534 0.2100 -0.0619 0.0136
## scale1 0.0058 0.0040 1.4410 0.1496 -0.0021 0.0136
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Quadratic with gender model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 13.5230 -27.0461 -23.0461 -21.7680 -21.9552
##
## tau^2 (estimated amount of total heterogeneity): 0.0051 (SE = 0.0027)
## tau (square root of estimated tau^2 value): 0.0716
## I^2 (total heterogeneity / total variability): 97.28%
## H^2 (total variability / sampling variability): 36.78
##
## Test for Heterogeneity:
## Q(df = 14) = 124.6455, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1323 0.0223 -5.9253 <.0001 -0.1761 -0.0885 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Random-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 35.1546 -70.3092 -66.3092 -65.0311 -65.2183
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0158
## I^2 (total heterogeneity / total variability): 89.65%
## H^2 (total variability / sampling variability): 9.66
##
## Test for Heterogeneity:
## Q(df = 14) = 299.0124, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0060 0.0052 1.1564 0.2475 -0.0042 0.0162
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.5939 -21.1878 -17.1878 -15.9097 -16.0969
##
## tau^2 (estimated amount of total heterogeneity): 0.0120 (SE = 0.0049)
## tau (square root of estimated tau^2 value): 0.1093
## I^2 (total heterogeneity / total variability): 96.08%
## H^2 (total variability / sampling variability): 25.50
##
## Test for Heterogeneity:
## Q(df = 14) = 664.8119, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2660 0.0293 -9.0709 <.0001 -0.3235 -0.2085 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 12.3685 -24.7371 -18.7371 -17.0422 -16.0704
##
## tau^2 (estimated amount of residual heterogeneity): 0.0053 (SE = 0.0030)
## tau (square root of estimated tau^2 value): 0.0725
## I^2 (residual heterogeneity / unaccounted variability): 92.60%
## H^2 (unaccounted variability / sampling variability): 13.51
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 72.8420, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.0691, p-val = 0.3011
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0612 0.0726 -0.8434 0.3990 -0.2034 0.0810
## continentEurope -0.0789 0.0763 -1.0340 0.3011 -0.2285 0.0707
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 33.8589 -67.7179 -61.7179 -60.0230 -59.0512
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0116
## I^2 (residual heterogeneity / unaccounted variability): 64.79%
## H^2 (unaccounted variability / sampling variability): 2.84
## R^2 (amount of heterogeneity accounted for): 45.92%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 26.7300, p-val = 0.0135
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 5.8170, p-val = 0.0159
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0208 0.0117 -1.7801 0.0751 -0.0437 0.0021 .
## continentEurope 0.0303 0.0125 2.4118 0.0159 0.0057 0.0549 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.1987 -22.3973 -16.3973 -14.7025 -13.7306
##
## tau^2 (estimated amount of residual heterogeneity): 0.0094 (SE = 0.0041)
## tau (square root of estimated tau^2 value): 0.0967
## I^2 (residual heterogeneity / unaccounted variability): 92.87%
## H^2 (unaccounted variability / sampling variability): 14.03
## R^2 (amount of heterogeneity accounted for): 21.74%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 214.6531, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.4204, p-val = 0.0355
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0688 0.0970 -0.7098 0.4778 -0.2589 0.1213
## continentEurope -0.2118 0.1007 -2.1025 0.0355 -0.4092 -0.0144 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 13.4378 -26.8755 -20.8755 -19.1807 -18.2089
##
## tau^2 (estimated amount of residual heterogeneity): 0.0037 (SE = 0.0023)
## tau (square root of estimated tau^2 value): 0.0608
## I^2 (residual heterogeneity / unaccounted variability): 95.64%
## H^2 (unaccounted variability / sampling variability): 22.95
## R^2 (amount of heterogeneity accounted for): 27.88%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 81.2111, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.7121, p-val = 0.0540
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2092 0.1767 1.1838 0.2365 -0.1372 0.5557
## mean.age -0.0056 0.0029 -1.9267 0.0540 -0.0113 0.0001 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 32.2428 -64.4855 -58.4855 -56.7907 -55.8189
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0165
## I^2 (residual heterogeneity / unaccounted variability): 89.07%
## H^2 (unaccounted variability / sampling variability): 9.15
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 225.5028, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3945, p-val = 0.5299
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0239 0.0480 -0.4973 0.6190 -0.1180 0.0702
## mean.age 0.0005 0.0008 0.6281 0.5299 -0.0010 0.0020
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.3493 -18.6985 -12.6985 -11.0037 -10.0319
##
## tau^2 (estimated amount of residual heterogeneity): 0.0129 (SE = 0.0055)
## tau (square root of estimated tau^2 value): 0.1138
## I^2 (residual heterogeneity / unaccounted variability): 95.52%
## H^2 (unaccounted variability / sampling variability): 22.32
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 551.1102, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0265, p-val = 0.8707
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3173 0.3149 -1.0075 0.3137 -0.9344 0.2999
## mean.age 0.0008 0.0050 0.1628 0.8707 -0.0091 0.0107
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 12.4323 -24.8646 -18.8646 -17.1697 -16.1979
##
## tau^2 (estimated amount of residual heterogeneity): 0.0053 (SE = 0.0030)
## tau (square root of estimated tau^2 value): 0.0725
## I^2 (residual heterogeneity / unaccounted variability): 93.00%
## H^2 (unaccounted variability / sampling variability): 14.29
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 90.4281, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.1533, p-val = 0.2829
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2262 0.0901 -2.5098 0.0121 -0.4029 -0.0496 *
## scale1 0.0205 0.0191 1.0739 0.2829 -0.0169 0.0579
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 32.5294 -65.0588 -59.0588 -57.3639 -56.3921
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0153
## I^2 (residual heterogeneity / unaccounted variability): 78.77%
## H^2 (unaccounted variability / sampling variability): 4.71
## R^2 (amount of heterogeneity accounted for): 6.24%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 60.3331, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.1434, p-val = 0.2849
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0147 0.0199 -0.7347 0.4625 -0.0537 0.0244
## scale1 0.0045 0.0042 1.0693 0.2849 -0.0037 0.0126
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.5009 -21.0019 -15.0019 -13.3070 -12.3352
##
## tau^2 (estimated amount of residual heterogeneity): 0.0104 (SE = 0.0045)
## tau (square root of estimated tau^2 value): 0.1020
## I^2 (residual heterogeneity / unaccounted variability): 95.12%
## H^2 (unaccounted variability / sampling variability): 20.51
## R^2 (amount of heterogeneity accounted for): 12.93%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 352.1781, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.6174, p-val = 0.1057
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0767 0.1200 -0.6390 0.5228 -0.3118 0.1585
## scale1 -0.0427 0.0264 -1.6178 0.1057 -0.0945 0.0090
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.3. Driving risk-taking
Intercept only model
Meta analysis:
ICC’s results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.5174 -5.0348 -1.0348 -3.6485 10.9652
##
## tau^2 (estimated amount of total heterogeneity): 0.0047 (SE = 0.0048)
## tau (square root of estimated tau^2 value): 0.0684
## I^2 (total heterogeneity / total variability): 98.75%
## H^2 (total variability / sampling variability): 79.85
##
## Test for Heterogeneity:
## Q(df = 2) = 220.7702, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4651 0.0398 11.6848 <.0001 0.3871 0.5431 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 0.9190 -1.8381 4.1619 -1.8381 28.1619
##
## tau^2 (estimated amount of residual heterogeneity): 0.0093 (SE = 0.0132)
## tau (square root of estimated tau^2 value): 0.0963
## I^2 (residual heterogeneity / unaccounted variability): 99.54%
## H^2 (unaccounted variability / sampling variability): 215.12
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 215.1215, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0072, p-val = 0.9326
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4717 0.0970 4.8610 <.0001 0.2815 0.6619 ***
## continentEurope -0.0100 0.1186 -0.0846 0.9326 -0.2426 0.2225
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 0.9219 -1.8439 4.1561 -1.8439 28.1561
##
## tau^2 (estimated amount of residual heterogeneity): 0.0092 (SE = 0.0131)
## tau (square root of estimated tau^2 value): 0.0960
## I^2 (residual heterogeneity / unaccounted variability): 99.44%
## H^2 (unaccounted variability / sampling variability): 179.17
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 179.1707, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0139, p-val = 0.9062
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5096 0.3830 1.3307 0.1833 -0.2410 1.2603
## mean.age -0.0008 0.0068 -0.1179 0.9062 -0.0142 0.0125
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.7744 -3.5487 2.4513 -3.5487 26.4513
##
## tau^2 (estimated amount of residual heterogeneity): 0.0016 (SE = 0.0024)
## tau (square root of estimated tau^2 value): 0.0400
## I^2 (residual heterogeneity / unaccounted variability): 95.22%
## H^2 (unaccounted variability / sampling variability): 20.91
## R^2 (amount of heterogeneity accounted for): 65.71%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 20.9150, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.6902, p-val = 0.0303
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2032 0.1234 1.6474 0.0995 -0.0386 0.4450 .
## scale2 0.0271 0.0125 2.1657 0.0303 0.0026 0.0517 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fixed effect model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.6356 -9.2713 -5.2713 -7.8850 6.7287
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0229
## I^2 (total heterogeneity / total variability): 90.47%
## H^2 (total variability / sampling variability): 10.50
##
## Test for Heterogeneity:
## Q(df = 2) = 28.1947, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1000 0.0141 -7.0814 <.0001 -0.1277 -0.0723 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1103 -4.2205 1.7795 -4.2205 25.7795
##
## tau^2 (estimated amount of residual heterogeneity): 0.0008 (SE = 0.0012)
## tau (square root of estimated tau^2 value): 0.0287
## I^2 (residual heterogeneity / unaccounted variability): 95.70%
## H^2 (unaccounted variability / sampling variability): 23.24
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 23.2427, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2290, p-val = 0.6323
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0869 0.0315 -2.7587 0.0058 -0.1487 -0.0252 **
## continentEurope -0.0180 0.0377 -0.4785 0.6323 -0.0920 0.0559
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.2352 -4.4705 1.5295 -4.4705 25.5295
##
## tau^2 (estimated amount of residual heterogeneity): 0.0006 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0250
## I^2 (residual heterogeneity / unaccounted variability): 93.20%
## H^2 (unaccounted variability / sampling variability): 14.70
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.7023, p-val = 0.0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5955, p-val = 0.4403
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1812 0.1066 -1.6997 0.0892 -0.3902 0.0278 .
## mean.age 0.0015 0.0019 0.7717 0.4403 -0.0023 0.0052
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1912 -4.3825 1.6175 -4.3825 25.6175
##
## tau^2 (estimated amount of residual heterogeneity): 0.0006 (SE = 0.0010)
## tau (square root of estimated tau^2 value): 0.0253
## I^2 (residual heterogeneity / unaccounted variability): 87.39%
## H^2 (unaccounted variability / sampling variability): 7.93
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 7.9286, p-val = 0.0049
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5605, p-val = 0.4540
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0410 0.0799 -0.5134 0.6077 -0.1977 0.1156
## scale2 -0.0061 0.0081 -0.7487 0.4540 -0.0220 0.0099
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.4849 -8.9698 -4.9698 -7.5835 7.0302
##
## tau^2 (estimated amount of total heterogeneity): 0.0006 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0249
## I^2 (total heterogeneity / total variability): 91.76%
## H^2 (total variability / sampling variability): 12.13
##
## Test for Heterogeneity:
## Q(df = 2) = 33.4609, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1003 0.0152 -6.5945 <.0001 -0.1300 -0.0705 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.0044 -4.0088 1.9912 -4.0088 25.9912
##
## tau^2 (estimated amount of residual heterogeneity): 0.0010 (SE = 0.0015)
## tau (square root of estimated tau^2 value): 0.0320
## I^2 (residual heterogeneity / unaccounted variability): 96.45%
## H^2 (unaccounted variability / sampling variability): 28.18
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 28.1843, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1685, p-val = 0.6814
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0880 0.0345 -2.5500 0.0108 -0.1557 -0.0204 *
## continentEurope -0.0170 0.0415 -0.4105 0.6814 -0.0984 0.0643
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1129 -4.2259 1.7741 -4.2259 25.7741
##
## tau^2 (estimated amount of residual heterogeneity): 0.0008 (SE = 0.0012)
## tau (square root of estimated tau^2 value): 0.0284
## I^2 (residual heterogeneity / unaccounted variability): 94.58%
## H^2 (unaccounted variability / sampling variability): 18.46
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 18.4648, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4657, p-val = 0.4950
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1806 0.1194 -1.5125 0.1304 -0.4147 0.0534
## mean.age 0.0015 0.0021 0.6824 0.4950 -0.0027 0.0057
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1568 -4.3136 1.6864 -4.3136 25.6864
##
## tau^2 (estimated amount of residual heterogeneity): 0.0007 (SE = 0.0011)
## tau (square root of estimated tau^2 value): 0.0263
## I^2 (residual heterogeneity / unaccounted variability): 88.47%
## H^2 (unaccounted variability / sampling variability): 8.67
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 8.6732, p-val = 0.0032
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6974, p-val = 0.4037
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0321 0.0830 -0.3867 0.6990 -0.1948 0.1306
## scale2 -0.0071 0.0084 -0.8351 0.4037 -0.0236 0.0095
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.4676 -8.9352 -4.9352 -7.5489 7.0648
##
## tau^2 (estimated amount of total heterogeneity): 0.0006 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0251
## I^2 (total heterogeneity / total variability): 92.27%
## H^2 (total variability / sampling variability): 12.94
##
## Test for Heterogeneity:
## Q(df = 2) = 35.9401, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1074 0.0153 -7.0185 <.0001 -0.1374 -0.0774 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.7846 -5.5692 -1.5692 -4.1829 10.4308
##
## tau^2 (estimated amount of total heterogeneity): 0.0031 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0560
## I^2 (total heterogeneity / total variability): 88.84%
## H^2 (total variability / sampling variability): 8.96
##
## Test for Heterogeneity:
## Q(df = 2) = 16.3845, p-val = 0.0003
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.3937 0.0344 -11.4325 <.0001 -0.4612 -0.3262 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.9907 -3.9814 2.0186 -3.9814 26.0186
##
## tau^2 (estimated amount of residual heterogeneity): 0.0011 (SE = 0.0015)
## tau (square root of estimated tau^2 value): 0.0325
## I^2 (residual heterogeneity / unaccounted variability): 96.69%
## H^2 (unaccounted variability / sampling variability): 30.22
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 30.2239, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1623, p-val = 0.6871
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0953 0.0348 -2.7384 0.0062 -0.1636 -0.0271 **
## continentEurope -0.0169 0.0419 -0.4028 0.6871 -0.0991 0.0653
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.1383 -2.2766 3.7234 -2.2766 27.7234
##
## tau^2 (estimated amount of residual heterogeneity): 0.0056 (SE = 0.0085)
## tau (square root of estimated tau^2 value): 0.0751
## I^2 (residual heterogeneity / unaccounted variability): 93.79%
## H^2 (unaccounted variability / sampling variability): 16.11
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 16.1108, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1979, p-val = 0.6564
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4218 0.0787 -5.3571 <.0001 -0.5761 -0.2675 ***
## continentEurope 0.0427 0.0959 0.4449 0.6564 -0.1453 0.2307
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.0972 -4.1945 1.8055 -4.1945 25.8055
##
## tau^2 (estimated amount of residual heterogeneity): 0.0008 (SE = 0.0012)
## tau (square root of estimated tau^2 value): 0.0290
## I^2 (residual heterogeneity / unaccounted variability): 94.97%
## H^2 (unaccounted variability / sampling variability): 19.90
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 19.8961, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4514, p-val = 0.5017
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1876 0.1210 -1.5506 0.1210 -0.4246 0.0495
## mean.age 0.0015 0.0022 0.6718 0.5017 -0.0028 0.0057
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.0673 -2.1346 3.8654 -2.1346 27.8654
##
## tau^2 (estimated amount of residual heterogeneity): 0.0065 (SE = 0.0098)
## tau (square root of estimated tau^2 value): 0.0806
## I^2 (residual heterogeneity / unaccounted variability): 93.86%
## H^2 (unaccounted variability / sampling variability): 16.28
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 16.2811, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0496, p-val = 0.8238
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3202 0.3301 -0.9698 0.3321 -0.9672 0.3269
## mean.age -0.0013 0.0059 -0.2226 0.8238 -0.0128 0.0102
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1519 -4.3038 1.6962 -4.3038 25.6962
##
## tau^2 (estimated amount of residual heterogeneity): 0.0007 (SE = 0.0011)
## tau (square root of estimated tau^2 value): 0.0266
## I^2 (residual heterogeneity / unaccounted variability): 89.30%
## H^2 (unaccounted variability / sampling variability): 9.35
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 9.3483, p-val = 0.0022
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7082, p-val = 0.4001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0383 0.0836 -0.4576 0.6472 -0.2021 0.1256
## scale2 -0.0072 0.0085 -0.8415 0.4001 -0.0238 0.0095
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.9697 -5.9394 0.0606 -5.9394 24.0606
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.1442, p-val = 0.7041
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 16.2403, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1346 0.0692 -1.9432 0.0520 -0.2703 0.0012 .
## scale2 -0.0268 0.0067 -4.0299 <.0001 -0.0399 -0.0138 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender interaction model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 5.0033 -10.0066 -6.0066 -8.6203 5.9934
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0160
## I^2 (total heterogeneity / total variability): 70.46%
## H^2 (total variability / sampling variability): 3.39
##
## Test for Heterogeneity:
## Q(df = 2) = 7.1299, p-val = 0.0283
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1250 0.0113 -11.0163 <.0001 -0.1472 -0.1027 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.8701 -5.7402 -1.7402 -4.3539 10.2598
##
## tau^2 (estimated amount of total heterogeneity): 0.0024 (SE = 0.0034)
## tau (square root of estimated tau^2 value): 0.0491
## I^2 (total heterogeneity / total variability): 78.69%
## H^2 (total variability / sampling variability): 4.69
##
## Test for Heterogeneity:
## Q(df = 2) = 10.3844, p-val = 0.0056
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.4037 0.0334 -12.0808 <.0001 -0.4691 -0.3382 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.2941 -8.5881 -4.5881 -7.2018 7.4119
##
## tau^2 (estimated amount of total heterogeneity): 0.0006 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0239
## I^2 (total heterogeneity / total variability): 72.71%
## H^2 (total variability / sampling variability): 3.66
##
## Test for Heterogeneity:
## Q(df = 2) = 7.9259, p-val = 0.0190
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0284 0.0165 1.7193 0.0856 -0.0040 0.0608 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.6212 -5.2424 0.7576 -5.2424 24.7576
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0155
## I^2 (residual heterogeneity / unaccounted variability): 77.83%
## H^2 (unaccounted variability / sampling variability): 4.51
## R^2 (amount of heterogeneity accounted for): 6.21%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 4.5101, p-val = 0.0337
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.9489, p-val = 0.3300
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1030 0.0253 -4.0791 <.0001 -0.1525 -0.0535 ***
## continentEurope -0.0274 0.0281 -0.9741 0.3300 -0.0825 0.0277
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3515 -2.7030 3.2970 -2.7030 27.2970
##
## tau^2 (estimated amount of residual heterogeneity): 0.0035 (SE = 0.0055)
## tau (square root of estimated tau^2 value): 0.0594
## I^2 (residual heterogeneity / unaccounted variability): 90.07%
## H^2 (unaccounted variability / sampling variability): 10.07
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 10.0712, p-val = 0.0015
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3538, p-val = 0.5520
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4474 0.0823 -5.4365 <.0001 -0.6087 -0.2861 ***
## continentEurope 0.0556 0.0934 0.5948 0.5520 -0.1275 0.2387
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0010 (SE = 0.0016)
## tau (square root of estimated tau^2 value): 0.0314
## I^2 (residual heterogeneity / unaccounted variability): 87.21%
## H^2 (unaccounted variability / sampling variability): 7.82
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 7.8207, p-val = 0.0052
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2075, p-val = 0.6487
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0127 0.0404 0.3130 0.7543 -0.0666 0.0919
## continentEurope 0.0213 0.0469 0.4555 0.6487 -0.0705 0.1132
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.9205 -5.8410 0.1590 -5.8410 24.1590
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0091
## I^2 (residual heterogeneity / unaccounted variability): 49.10%
## H^2 (unaccounted variability / sampling variability): 1.96
## R^2 (amount of heterogeneity accounted for): 67.48%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.9648, p-val = 0.1610
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.5680, p-val = 0.1090
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2345 0.0666 -3.5178 0.0004 -0.3651 -0.1038 ***
## mean.age 0.0021 0.0013 1.6025 0.1090 -0.0005 0.0046
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2283 -2.4566 3.5434 -2.4566 27.5434
##
## tau^2 (estimated amount of residual heterogeneity): 0.0045 (SE = 0.0071)
## tau (square root of estimated tau^2 value): 0.0671
## I^2 (residual heterogeneity / unaccounted variability): 89.72%
## H^2 (unaccounted variability / sampling variability): 9.72
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 9.7233, p-val = 0.0018
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1106, p-val = 0.7395
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3017 0.3131 -0.9635 0.3353 -0.9154 0.3120
## mean.age -0.0019 0.0057 -0.3325 0.7395 -0.0132 0.0093
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0012 (SE = 0.0019)
## tau (square root of estimated tau^2 value): 0.0342
## I^2 (residual heterogeneity / unaccounted variability): 86.79%
## H^2 (unaccounted variability / sampling variability): 7.57
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 7.5711, p-val = 0.0059
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0479, p-val = 0.8267
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0623 0.1555 0.4004 0.6889 -0.2426 0.3671
## mean.age -0.0006 0.0028 -0.2189 0.8267 -0.0062 0.0049
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1930 -4.3861 1.6139 -4.3861 25.6139
##
## tau^2 (estimated amount of residual heterogeneity): 0.0005 (SE = 0.0010)
## tau (square root of estimated tau^2 value): 0.0227
## I^2 (residual heterogeneity / unaccounted variability): 70.85%
## H^2 (unaccounted variability / sampling variability): 3.43
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 3.4308, p-val = 0.0640
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1168, p-val = 0.7325
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0977 0.0760 -1.2858 0.1985 -0.2467 0.0512
## scale2 -0.0027 0.0078 -0.3418 0.7325 -0.0179 0.0126
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.2410 -4.4819 1.5181 -4.4819 25.5181
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0024)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.0561, p-val = 0.8127
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 10.3282, p-val = 0.0013
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1888 0.0716 -2.6370 0.0084 -0.3292 -0.0485 **
## scale2 -0.0223 0.0069 -3.2138 0.0013 -0.0359 -0.0087 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.0544 -6.1088 -0.1088 -6.1088 23.8912
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.0004, p-val = 0.9842
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 7.9255, p-val = 0.0049
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1427 0.0438 3.2562 0.0011 0.0568 0.2286 **
## scale2 -0.0119 0.0042 -2.8152 0.0049 -0.0201 -0.0036 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.4. Recreational risk-taking
Intercept only model
Meta analysis:
ICC’s results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.2325 -6.4650 -2.4650 -5.0787 9.5350
##
## tau^2 (estimated amount of total heterogeneity): 0.0023 (SE = 0.0023)
## tau (square root of estimated tau^2 value): 0.0477
## I^2 (total heterogeneity / total variability): 97.85%
## H^2 (total variability / sampling variability): 46.54
##
## Test for Heterogeneity:
## Q(df = 2) = 139.6978, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4689 0.0280 16.7652 <.0001 0.4141 0.5237 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2832 -2.5664 3.4336 -2.5664 27.4336
##
## tau^2 (estimated amount of residual heterogeneity): 0.0045 (SE = 0.0064)
## tau (square root of estimated tau^2 value): 0.0668
## I^2 (residual heterogeneity / unaccounted variability): 99.22%
## H^2 (unaccounted variability / sampling variability): 129.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 129.0015, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0112, p-val = 0.9157
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4628 0.0679 6.8192 <.0001 0.3298 0.5958 ***
## continentEurope 0.0088 0.0828 0.1059 0.9157 -0.1535 0.1710
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3249 -2.6498 3.3502 -2.6498 27.3502
##
## tau^2 (estimated amount of residual heterogeneity): 0.0041 (SE = 0.0059)
## tau (square root of estimated tau^2 value): 0.0640
## I^2 (residual heterogeneity / unaccounted variability): 98.96%
## H^2 (unaccounted variability / sampling variability): 96.17
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 96.1704, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1014, p-val = 0.7502
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5496 0.2567 2.1409 0.0323 0.0464 1.0527 *
## mean.age -0.0015 0.0046 -0.3184 0.7502 -0.0104 0.0075
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.8111 -3.6222 2.3778 -3.6222 26.3778
##
## tau^2 (estimated amount of residual heterogeneity): 0.0015 (SE = 0.0022)
## tau (square root of estimated tau^2 value): 0.0386
## I^2 (residual heterogeneity / unaccounted variability): 95.07%
## H^2 (unaccounted variability / sampling variability): 20.29
## R^2 (amount of heterogeneity accounted for): 34.67%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 20.2865, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.9899, p-val = 0.1583
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3048 0.1187 2.5685 0.0102 0.0722 0.5374 *
## scale1 0.0170 0.0121 1.4107 0.1583 -0.0066 0.0406
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fixed effect model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.5828 -7.1656 -3.1656 -5.7793 8.8344
##
## tau^2 (estimated amount of total heterogeneity): 0.0016 (SE = 0.0017)
## tau (square root of estimated tau^2 value): 0.0397
## I^2 (total heterogeneity / total variability): 96.50%
## H^2 (total variability / sampling variability): 28.59
##
## Test for Heterogeneity:
## Q(df = 2) = 79.5694, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1674 0.0235 -7.1081 <.0001 -0.2135 -0.1212 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5967 -3.1933 2.8067 -3.1933 26.8067
##
## tau^2 (estimated amount of residual heterogeneity): 0.0024 (SE = 0.0034)
## tau (square root of estimated tau^2 value): 0.0486
## I^2 (residual heterogeneity / unaccounted variability): 98.46%
## H^2 (unaccounted variability / sampling variability): 64.77
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 64.7722, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3148, p-val = 0.5747
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1436 0.0506 -2.8352 0.0046 -0.2428 -0.0443 **
## continentEurope -0.0344 0.0614 -0.5611 0.5747 -0.1547 0.0858
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.7400 -3.4800 2.5200 -3.4800 26.5200
##
## tau^2 (estimated amount of residual heterogeneity): 0.0018 (SE = 0.0026)
## tau (square root of estimated tau^2 value): 0.0419
## I^2 (residual heterogeneity / unaccounted variability): 97.41%
## H^2 (unaccounted variability / sampling variability): 38.67
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 38.6651, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7574, p-val = 0.3842
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3151 0.1717 -1.8354 0.0664 -0.6516 0.0214 .
## mean.age 0.0027 0.0031 0.8703 0.3842 -0.0033 0.0087
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6048 -3.2096 2.7904 -3.2096 26.7904
##
## tau^2 (estimated amount of residual heterogeneity): 0.0023 (SE = 0.0033)
## tau (square root of estimated tau^2 value): 0.0475
## I^2 (residual heterogeneity / unaccounted variability): 95.49%
## H^2 (unaccounted variability / sampling variability): 22.19
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 22.1883, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3765, p-val = 0.5395
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0795 0.1454 -0.5469 0.5845 -0.3645 0.2055
## scale1 -0.0091 0.0148 -0.6136 0.5395 -0.0381 0.0199
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.4996 -6.9992 -2.9992 -5.6129 9.0008
##
## tau^2 (estimated amount of total heterogeneity): 0.0017 (SE = 0.0018)
## tau (square root of estimated tau^2 value): 0.0415
## I^2 (total heterogeneity / total variability): 96.82%
## H^2 (total variability / sampling variability): 31.41
##
## Test for Heterogeneity:
## Q(df = 2) = 91.5192, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1648 0.0246 -6.7105 <.0001 -0.2130 -0.1167 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5080 -3.0160 2.9840 -3.0160 26.9840
##
## tau^2 (estimated amount of residual heterogeneity): 0.0028 (SE = 0.0041)
## tau (square root of estimated tau^2 value): 0.0532
## I^2 (residual heterogeneity / unaccounted variability): 98.72%
## H^2 (unaccounted variability / sampling variability): 78.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 77.9964, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1987, p-val = 0.6558
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1443 0.0550 -2.6214 0.0088 -0.2522 -0.0364 **
## continentEurope -0.0298 0.0668 -0.4457 0.6558 -0.1607 0.1012
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6244 -3.2487 2.7513 -3.2487 26.7513
##
## tau^2 (estimated amount of residual heterogeneity): 0.0022 (SE = 0.0032)
## tau (square root of estimated tau^2 value): 0.0472
## I^2 (residual heterogeneity / unaccounted variability): 97.96%
## H^2 (unaccounted variability / sampling variability): 49.13
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 49.1349, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5181, p-val = 0.4717
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3014 0.1920 -1.5696 0.1165 -0.6778 0.0750
## mean.age 0.0025 0.0034 0.7198 0.4717 -0.0043 0.0092
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6255 -3.2510 2.7490 -3.2510 26.7490
##
## tau^2 (estimated amount of residual heterogeneity): 0.0022 (SE = 0.0032)
## tau (square root of estimated tau^2 value): 0.0465
## I^2 (residual heterogeneity / unaccounted variability): 95.33%
## H^2 (unaccounted variability / sampling variability): 21.43
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 21.4300, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5626, p-val = 0.4532
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0599 0.1424 -0.4206 0.6741 -0.3389 0.2192
## scale1 -0.0109 0.0145 -0.7501 0.4532 -0.0392 0.0175
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6536 -7.3073 -3.3073 -5.9210 8.6927
##
## tau^2 (estimated amount of total heterogeneity): 0.0015 (SE = 0.0016)
## tau (square root of estimated tau^2 value): 0.0384
## I^2 (total heterogeneity / total variability): 96.43%
## H^2 (total variability / sampling variability): 28.02
##
## Test for Heterogeneity:
## Q(df = 2) = 82.9062, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1703 0.0228 -7.4715 <.0001 -0.2149 -0.1256 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.7562 -5.5124 -1.5124 -4.1261 10.4876
##
## tau^2 (estimated amount of total heterogeneity): 0.0031 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0556
## I^2 (total heterogeneity / total variability): 88.03%
## H^2 (total variability / sampling variability): 8.35
##
## Test for Heterogeneity:
## Q(df = 2) = 12.5156, p-val = 0.0019
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.3731 0.0344 -10.8491 <.0001 -0.4404 -0.3057 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5614 -3.1228 2.8772 -3.1228 26.8772
##
## tau^2 (estimated amount of residual heterogeneity): 0.0025 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0504
## I^2 (residual heterogeneity / unaccounted variability): 98.61%
## H^2 (unaccounted variability / sampling variability): 72.15
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 72.1466, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1397, p-val = 0.7086
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1538 0.0522 -2.9450 0.0032 -0.2562 -0.0515 **
## continentEurope -0.0237 0.0634 -0.3738 0.7086 -0.1479 0.1005
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1419 -4.2838 1.7162 -4.2838 25.7162
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0011)
## tau (square root of estimated tau^2 value): 0.0207
## I^2 (residual heterogeneity / unaccounted variability): 53.32%
## H^2 (unaccounted variability / sampling variability): 2.14
## R^2 (amount of heterogeneity accounted for): 86.06%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.1424, p-val = 0.1433
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 6.3646, p-val = 0.0116
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4421 0.0331 -13.3624 <.0001 -0.5070 -0.3773 ***
## continentEurope 0.0967 0.0383 2.5228 0.0116 0.0216 0.1718 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6615 -3.3229 2.6771 -3.3229 26.6771
##
## tau^2 (estimated amount of residual heterogeneity): 0.0021 (SE = 0.0030)
## tau (square root of estimated tau^2 value): 0.0455
## I^2 (residual heterogeneity / unaccounted variability): 97.88%
## H^2 (unaccounted variability / sampling variability): 47.10
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 47.0959, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3977, p-val = 0.5283
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2855 0.1850 -1.5430 0.1228 -0.6482 0.0772
## mean.age 0.0021 0.0033 0.6307 0.5283 -0.0044 0.0086
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6936 -3.3872 2.6128 -3.3872 26.6128
##
## tau^2 (estimated amount of residual heterogeneity): 0.0015 (SE = 0.0028)
## tau (square root of estimated tau^2 value): 0.0393
## I^2 (residual heterogeneity / unaccounted variability): 78.10%
## H^2 (unaccounted variability / sampling variability): 4.57
## R^2 (amount of heterogeneity accounted for): 49.96%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 4.5659, p-val = 0.0326
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.5750, p-val = 0.1086
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0907 0.1773 -0.5117 0.6089 -0.4383 0.2568
## mean.age -0.0051 0.0032 -1.6047 0.1086 -0.0114 0.0011
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.7531 -3.5062 2.4938 -3.5062 26.4938
##
## tau^2 (estimated amount of residual heterogeneity): 0.0017 (SE = 0.0025)
## tau (square root of estimated tau^2 value): 0.0407
## I^2 (residual heterogeneity / unaccounted variability): 94.33%
## H^2 (unaccounted variability / sampling variability): 17.64
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 17.6441, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7341, p-val = 0.3915
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0650 0.1251 -0.5191 0.6037 -0.3102 0.1803
## scale1 -0.0109 0.0127 -0.8568 0.3915 -0.0359 0.0140
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4286 -2.8572 3.1428 -2.8572 27.1428
##
## tau^2 (estimated amount of residual heterogeneity): 0.0030 (SE = 0.0048)
## tau (square root of estimated tau^2 value): 0.0543
## I^2 (residual heterogeneity / unaccounted variability): 87.74%
## H^2 (unaccounted variability / sampling variability): 8.16
## R^2 (amount of heterogeneity accounted for): 4.49%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 8.1573, p-val = 0.0043
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.1699, p-val = 0.2794
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1832 0.1787 -1.0256 0.3051 -0.5334 0.1669
## scale1 -0.0195 0.0181 -1.0816 0.2794 -0.0549 0.0159
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender interaction model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.3138 -6.6277 -2.6277 -5.2414 9.3723
##
## tau^2 (estimated amount of total heterogeneity): 0.0020 (SE = 0.0022)
## tau (square root of estimated tau^2 value): 0.0443
## I^2 (total heterogeneity / total variability): 94.60%
## H^2 (total variability / sampling variability): 18.51
##
## Test for Heterogeneity:
## Q(df = 2) = 42.6115, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1838 0.0268 -6.8594 <.0001 -0.2363 -0.1313 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.2999 -8.5998 -4.5998 -7.2135 7.4002
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 2) = 1.5405, p-val = 0.4629
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.3629 0.0111 -32.5617 <.0001 -0.3847 -0.3410 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.0570 -8.1139 -4.1139 -6.7276 7.8861
##
## tau^2 (estimated amount of total heterogeneity): 0.0004 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0190
## I^2 (total heterogeneity / total variability): 62.05%
## H^2 (total variability / sampling variability): 2.64
##
## Test for Heterogeneity:
## Q(df = 2) = 4.6848, p-val = 0.0961
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0331 0.0142 2.3373 0.0194 0.0053 0.0608 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6478 -3.2956 2.7044 -3.2956 26.7044
##
## tau^2 (estimated amount of residual heterogeneity): 0.0021 (SE = 0.0031)
## tau (square root of estimated tau^2 value): 0.0458
## I^2 (residual heterogeneity / unaccounted variability): 96.80%
## H^2 (unaccounted variability / sampling variability): 31.22
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 31.2172, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8513, p-val = 0.3562
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1444 0.0507 -2.8492 0.0044 -0.2438 -0.0451 **
## continentEurope -0.0558 0.0604 -0.9226 0.3562 -0.1742 0.0627
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.5625 -5.1250 0.8750 -5.1250 24.8750
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.8746, p-val = 0.3497
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6659, p-val = 0.4145
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4115 0.0606 -6.7919 <.0001 -0.5302 -0.2927 ***
## continentEurope 0.0503 0.0616 0.8160 0.4145 -0.0705 0.1711
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.9403, p-val = 0.3322
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.7445, p-val = 0.0530
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0156 0.0279 -0.5595 0.5758 -0.0702 0.0390
## continentEurope 0.0554 0.0286 1.9351 0.0530 -0.0007 0.1115 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.8875 -3.7750 2.2250 -3.7750 26.2250
##
## tau^2 (estimated amount of residual heterogeneity): 0.0013 (SE = 0.0019)
## tau (square root of estimated tau^2 value): 0.0354
## I^2 (residual heterogeneity / unaccounted variability): 93.33%
## H^2 (unaccounted variability / sampling variability): 15.00
## R^2 (amount of heterogeneity accounted for): 36.27%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 15.0031, p-val = 0.0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.9631, p-val = 0.1612
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3990 0.1542 -2.5864 0.0097 -0.7013 -0.0966 **
## mean.age 0.0039 0.0028 1.4011 0.1612 -0.0016 0.0094
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1685 -4.3370 1.6630 -4.3370 25.6630
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0011)
## tau (square root of estimated tau^2 value): 0.0152
## I^2 (residual heterogeneity / unaccounted variability): 30.20%
## H^2 (unaccounted variability / sampling variability): 1.43
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.4326, p-val = 0.2313
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2291, p-val = 0.6322
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2805 0.1704 -1.6466 0.0996 -0.6145 0.0534 .
## mean.age -0.0016 0.0034 -0.4786 0.6322 -0.0082 0.0050
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0164
## I^2 (residual heterogeneity / unaccounted variability): 60.09%
## H^2 (unaccounted variability / sampling variability): 2.51
## R^2 (amount of heterogeneity accounted for): 24.85%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.5056, p-val = 0.1134
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.1016, p-val = 0.1471
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1816 0.1027 1.7682 0.0770 -0.0197 0.3829 .
## mean.age -0.0028 0.0019 -1.4497 0.1471 -0.0066 0.0010
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3573 -2.7146 3.2854 -2.7146 27.2854
##
## tau^2 (estimated amount of residual heterogeneity): 0.0036 (SE = 0.0055)
## tau (square root of estimated tau^2 value): 0.0602
## I^2 (residual heterogeneity / unaccounted variability): 93.59%
## H^2 (unaccounted variability / sampling variability): 15.59
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 15.5935, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1044, p-val = 0.7466
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1240 0.1849 -0.6706 0.5025 -0.4865 0.2385
## scale1 -0.0061 0.0188 -0.3231 0.7466 -0.0430 0.0308
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.9466 -3.8933 2.1067 -3.8933 26.1067
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0027)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.5265, p-val = 0.4681
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.0140, p-val = 0.3139
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2910 0.0723 -4.0269 <.0001 -0.4326 -0.1494 ***
## scale1 -0.0070 0.0070 -1.0070 0.3139 -0.0207 0.0067
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.8771 -3.7542 2.2458 -3.7542 26.2458
##
## tau^2 (estimated amount of residual heterogeneity): 0.0010 (SE = 0.0019)
## tau (square root of estimated tau^2 value): 0.0309
## I^2 (residual heterogeneity / unaccounted variability): 69.74%
## H^2 (unaccounted variability / sampling variability): 3.31
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 3.3052, p-val = 0.0691
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6957, p-val = 0.4042
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1155 0.1046 1.1045 0.2694 -0.0895 0.3206
## scale1 -0.0089 0.0107 -0.8341 0.4042 -0.0299 0.0120
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.5. Occupational risk-taking
Intercept only model
Meta analysis:
ICC’s results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.7927 -5.5854 -1.5854 -4.1991 10.4146
##
## tau^2 (estimated amount of total heterogeneity): 0.0035 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0593
## I^2 (total heterogeneity / total variability): 98.21%
## H^2 (total variability / sampling variability): 55.93
##
## Test for Heterogeneity:
## Q(df = 2) = 122.6868, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4098 0.0346 11.8396 <.0001 0.3419 0.4776 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.1260 -2.2520 3.7480 -2.2520 27.7480
##
## tau^2 (estimated amount of residual heterogeneity): 0.0061 (SE = 0.0087)
## tau (square root of estimated tau^2 value): 0.0782
## I^2 (residual heterogeneity / unaccounted variability): 99.18%
## H^2 (unaccounted variability / sampling variability): 121.89
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 121.8878, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1591, p-val = 0.6900
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4355 0.0789 5.5166 <.0001 0.2808 0.5902 ***
## continentEurope -0.0385 0.0965 -0.3989 0.6900 -0.2276 0.1506
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.0674 -2.1349 3.8651 -2.1349 27.8651
##
## tau^2 (estimated amount of residual heterogeneity): 0.0069 (SE = 0.0098)
## tau (square root of estimated tau^2 value): 0.0829
## I^2 (residual heterogeneity / unaccounted variability): 99.16%
## H^2 (unaccounted variability / sampling variability): 118.88
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 118.8819, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0325, p-val = 0.8570
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3507 0.3310 1.0595 0.2894 -0.2981 0.9995
## mean.age 0.0011 0.0059 0.1802 0.8570 -0.0105 0.0126
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.0155 -6.0310 -0.0310 -6.0310 23.9690
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0081
## I^2 (residual heterogeneity / unaccounted variability): 47.18%
## H^2 (unaccounted variability / sampling variability): 1.89
## R^2 (amount of heterogeneity accounted for): 98.11%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.8932, p-val = 0.1688
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 54.5587, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1566 0.0356 4.4007 <.0001 0.0869 0.2263 ***
## scale1 0.0264 0.0036 7.3864 <.0001 0.0194 0.0334 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fixed effect model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 5.0642 -10.1284 -6.1284 -8.7421 5.8716
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0180
## I^2 (total heterogeneity / total variability): 83.94%
## H^2 (total variability / sampling variability): 6.23
##
## Test for Heterogeneity:
## Q(df = 2) = 16.3123, p-val = 0.0003
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1747 0.0117 -14.9541 <.0001 -0.1976 -0.1518 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.2959 -4.5917 1.4083 -4.5917 25.4083
##
## tau^2 (estimated amount of residual heterogeneity): 0.0006 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0235
## I^2 (residual heterogeneity / unaccounted variability): 93.33%
## H^2 (unaccounted variability / sampling variability): 14.99
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.9881, p-val = 0.0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0666, p-val = 0.7964
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1680 0.0279 -6.0127 <.0001 -0.2228 -0.1133 ***
## continentEurope -0.0085 0.0328 -0.2581 0.7964 -0.0728 0.0559
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.3710 -4.7419 1.2581 -4.7419 25.2581
##
## tau^2 (estimated amount of residual heterogeneity): 0.0005 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0215
## I^2 (residual heterogeneity / unaccounted variability): 90.25%
## H^2 (unaccounted variability / sampling variability): 10.26
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 10.2557, p-val = 0.0014
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2645, p-val = 0.6070
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2233 0.0961 -2.3230 0.0202 -0.4117 -0.0349 *
## mean.age 0.0009 0.0017 0.5143 0.6070 -0.0025 0.0043
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.6155 -5.2309 0.7691 -5.2309 24.7691
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0138
## I^2 (residual heterogeneity / unaccounted variability): 61.08%
## H^2 (unaccounted variability / sampling variability): 2.57
## R^2 (amount of heterogeneity accounted for): 41.09%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.5691, p-val = 0.1090
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.7407, p-val = 0.1870
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1129 0.0484 -2.3303 0.0198 -0.2078 -0.0179 *
## scale1 -0.0065 0.0050 -1.3194 0.1870 -0.0162 0.0032
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 5.1853 -10.3707 -6.3707 -8.9844 5.6293
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0167
## I^2 (total heterogeneity / total variability): 81.87%
## H^2 (total variability / sampling variability): 5.52
##
## Test for Heterogeneity:
## Q(df = 2) = 14.2366, p-val = 0.0008
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1747 0.0110 -15.8989 <.0001 -0.1963 -0.1532 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.3642 -4.7284 1.2716 -4.7284 25.2716
##
## tau^2 (estimated amount of residual heterogeneity): 0.0005 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0219
## I^2 (residual heterogeneity / unaccounted variability): 92.36%
## H^2 (unaccounted variability / sampling variability): 13.08
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 13.0828, p-val = 0.0003
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0655, p-val = 0.7980
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1684 0.0265 -6.3510 <.0001 -0.2203 -0.1164 ***
## continentEurope -0.0079 0.0310 -0.2559 0.7980 -0.0687 0.0528
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.4388 -4.8776 1.1224 -4.8776 25.1224
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0199
## I^2 (residual heterogeneity / unaccounted variability): 88.86%
## H^2 (unaccounted variability / sampling variability): 8.98
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 8.9763, p-val = 0.0027
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2654, p-val = 0.6064
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2205 0.0905 -2.4360 0.0149 -0.3979 -0.0431 *
## mean.age 0.0008 0.0016 0.5152 0.6064 -0.0024 0.0041
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.6867 -5.3734 0.6266 -5.3734 24.6266
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0123
## I^2 (residual heterogeneity / unaccounted variability): 55.59%
## H^2 (unaccounted variability / sampling variability): 2.25
## R^2 (amount of heterogeneity accounted for): 46.03%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.2516, p-val = 0.1335
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.8955, p-val = 0.1686
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1158 0.0444 -2.6084 0.0091 -0.2027 -0.0288 **
## scale1 -0.0062 0.0045 -1.3768 0.1686 -0.0151 0.0026
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 5.3576 -10.7151 -6.7151 -9.3288 5.2849
##
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0151
## I^2 (total heterogeneity / total variability): 79.07%
## H^2 (total variability / sampling variability): 4.78
##
## Test for Heterogeneity:
## Q(df = 2) = 12.0238, p-val = 0.0024
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1793 0.0101 -17.7267 <.0001 -0.1992 -0.1595 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.7595 -5.5189 -1.5189 -4.1326 10.4811
##
## tau^2 (estimated amount of total heterogeneity): 0.0031 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0560
## I^2 (total heterogeneity / total variability): 87.81%
## H^2 (total variability / sampling variability): 8.21
##
## Test for Heterogeneity:
## Q(df = 2) = 14.3779, p-val = 0.0008
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2714 0.0347 -7.8218 <.0001 -0.3394 -0.2034 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.4320 -4.8639 1.1361 -4.8639 25.1361
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0203
## I^2 (residual heterogeneity / unaccounted variability): 91.41%
## H^2 (unaccounted variability / sampling variability): 11.65
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 11.6481, p-val = 0.0006
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0018, p-val = 0.9660
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1780 0.0251 -7.0862 <.0001 -0.2272 -0.1287 ***
## continentEurope -0.0012 0.0293 -0.0427 0.9660 -0.0586 0.0561
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2818 -2.5636 3.4364 -2.5636 27.4364
##
## tau^2 (estimated amount of residual heterogeneity): 0.0041 (SE = 0.0064)
## tau (square root of estimated tau^2 value): 0.0644
## I^2 (residual heterogeneity / unaccounted variability): 91.90%
## H^2 (unaccounted variability / sampling variability): 12.35
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 12.3468, p-val = 0.0004
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6069, p-val = 0.4360
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3165 0.0700 -4.5214 <.0001 -0.4536 -0.1793 ***
## continentEurope 0.0659 0.0846 0.7790 0.4360 -0.0999 0.2316
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.4562 -4.9123 1.0877 -4.9123 25.0877
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0195
## I^2 (residual heterogeneity / unaccounted variability): 88.69%
## H^2 (unaccounted variability / sampling variability): 8.84
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 8.8444, p-val = 0.0029
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0764, p-val = 0.7823
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2033 0.0889 -2.2857 0.0223 -0.3776 -0.0290 *
## mean.age 0.0004 0.0016 0.2764 0.7823 -0.0027 0.0036
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.1466 -2.2931 3.7069 -2.2931 27.7069
##
## tau^2 (estimated amount of residual heterogeneity): 0.0055 (SE = 0.0084)
## tau (square root of estimated tau^2 value): 0.0741
## I^2 (residual heterogeneity / unaccounted variability): 92.89%
## H^2 (unaccounted variability / sampling variability): 14.07
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.0712, p-val = 0.0002
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2502, p-val = 0.6169
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1191 0.3075 -0.3873 0.6985 -0.7217 0.4835
## mean.age -0.0027 0.0055 -0.5002 0.6169 -0.0135 0.0080
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.0928 -6.1857 -0.1857 -6.1857 23.8143
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0019
## I^2 (residual heterogeneity / unaccounted variability): 3.10%
## H^2 (unaccounted variability / sampling variability): 1.03
## R^2 (amount of heterogeneity accounted for): 98.36%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.0320, p-val = 0.3097
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 9.9312, p-val = 0.0016
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1130 0.0234 -4.8226 <.0001 -0.1590 -0.0671 ***
## scale1 -0.0072 0.0023 -3.1514 0.0016 -0.0117 -0.0027 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.6951 -5.3901 0.6099 -5.3901 24.6099
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.4535, p-val = 0.5007
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 13.9245, p-val = 0.0002
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0283 0.0684 -0.4139 0.6790 -0.1623 0.1057
## scale1 -0.0247 0.0066 -3.7316 0.0002 -0.0376 -0.0117 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender interaction model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 5.7723 -11.5446 -7.5446 -10.1583 4.4554
##
## tau^2 (estimated amount of total heterogeneity): 0.0000 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0005
## I^2 (total heterogeneity / total variability): 0.21%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 2) = 2.1896, p-val = 0.3346
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1834 0.0050 -36.7338 <.0001 -0.1932 -0.1736 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.8349 -5.6697 -1.6697 -4.2834 10.3303
##
## tau^2 (estimated amount of total heterogeneity): 0.0028 (SE = 0.0039)
## tau (square root of estimated tau^2 value): 0.0533
## I^2 (total heterogeneity / total variability): 81.59%
## H^2 (total variability / sampling variability): 5.43
##
## Test for Heterogeneity:
## Q(df = 2) = 13.8097, p-val = 0.0010
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2494 0.0360 -6.9232 <.0001 -0.3200 -0.1788 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6823 -7.3645 -3.3645 -5.9782 8.6355
##
## tau^2 (estimated amount of total heterogeneity): 0.0010 (SE = 0.0014)
## tau (square root of estimated tau^2 value): 0.0319
## I^2 (total heterogeneity / total variability): 80.61%
## H^2 (total variability / sampling variability): 5.16
##
## Test for Heterogeneity:
## Q(df = 2) = 9.3161, p-val = 0.0095
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0030 0.0211 -0.1422 0.8869 -0.0444 0.0384
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6864 -7.3729 -1.3729 -7.3729 22.6271
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.3067, p-val = 0.5797
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.8829, p-val = 0.1700
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1525 0.0231 -6.5998 <.0001 -0.1978 -0.1072 ***
## continentEurope -0.0325 0.0237 -1.3722 0.1700 -0.0788 0.0139
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2385 -2.4770 3.5230 -2.4770 27.5230
##
## tau^2 (estimated amount of residual heterogeneity): 0.0046 (SE = 0.0070)
## tau (square root of estimated tau^2 value): 0.0675
## I^2 (residual heterogeneity / unaccounted variability): 92.53%
## H^2 (unaccounted variability / sampling variability): 13.39
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 13.3916, p-val = 0.0003
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0275, p-val = 0.8683
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2344 0.0926 -2.5302 0.0114 -0.4160 -0.0528 *
## continentEurope -0.0174 0.1051 -0.1659 0.8683 -0.2233 0.1885
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0010 (SE = 0.0017)
## tau (square root of estimated tau^2 value): 0.0322
## I^2 (residual heterogeneity / unaccounted variability): 86.94%
## H^2 (unaccounted variability / sampling variability): 7.66
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 7.6565, p-val = 0.0057
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.0764, p-val = 0.2995
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0430 0.0440 -0.9770 0.3286 -0.1293 0.0433
## continentEurope 0.0522 0.0503 1.0375 0.2995 -0.0464 0.1508
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.7124 -7.4248 -1.4248 -7.4248 22.5752
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.0012, p-val = 0.9720
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.1884, p-val = 0.1391
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2718 0.0600 -4.5326 <.0001 -0.3894 -0.1543 ***
## mean.age 0.0018 0.0012 1.4793 0.1391 -0.0006 0.0041
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2962 -2.5925 3.4075 -2.5925 27.4075
##
## tau^2 (estimated amount of residual heterogeneity): 0.0039 (SE = 0.0062)
## tau (square root of estimated tau^2 value): 0.0622
## I^2 (residual heterogeneity / unaccounted variability): 88.39%
## H^2 (unaccounted variability / sampling variability): 8.62
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 8.6161, p-val = 0.0033
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1912, p-val = 0.6619
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3812 0.3065 -1.2438 0.2136 -0.9819 0.2195
## mean.age 0.0025 0.0057 0.4373 0.6619 -0.0086 0.0136
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0016 (SE = 0.0026)
## tau (square root of estimated tau^2 value): 0.0403
## I^2 (residual heterogeneity / unaccounted variability): 89.22%
## H^2 (unaccounted variability / sampling variability): 9.27
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 9.2723, p-val = 0.0023
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4880, p-val = 0.4848
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1224 0.1827 0.6702 0.5028 -0.2356 0.4805
## mean.age -0.0023 0.0033 -0.6986 0.4848 -0.0088 0.0042
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.3089 -4.6179 1.3821 -4.6179 25.3821
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0172
## I^2 (residual heterogeneity / unaccounted variability): 50.92%
## H^2 (unaccounted variability / sampling variability): 2.04
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.0375, p-val = 0.1535
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0134, p-val = 0.9080
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1849 0.0617 -2.9980 0.0027 -0.3058 -0.0640 **
## scale1 0.0007 0.0063 0.1156 0.9080 -0.0117 0.0132
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6602 -3.3204 2.6796 -3.3204 26.6796
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0030)
## tau (square root of estimated tau^2 value): 0.0037
## I^2 (residual heterogeneity / unaccounted variability): 0.66%
## H^2 (unaccounted variability / sampling variability): 1.01
## R^2 (amount of heterogeneity accounted for): 99.51%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.0066, p-val = 0.3157
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 12.2996, p-val = 0.0005
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0317 0.0700 -0.4535 0.6502 -0.1689 0.1054
## scale1 -0.0241 0.0069 -3.5071 0.0005 -0.0375 -0.0106 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.4443 -4.8886 1.1114 -4.8886 25.1114
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.9106, p-val = 0.3400
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 8.4055, p-val = 0.0037
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1244 0.0453 2.7456 0.0060 0.0356 0.2132 **
## scale1 -0.0127 0.0044 -2.8992 0.0037 -0.0213 -0.0041 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.6. Health risk-taking
Intercept only model
Meta analysis:
ICC’s results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.2961 -6.5922 -2.5922 -5.2059 9.4078
##
## tau^2 (estimated amount of total heterogeneity): 0.0021 (SE = 0.0022)
## tau (square root of estimated tau^2 value): 0.0458
## I^2 (total heterogeneity / total variability): 97.46%
## H^2 (total variability / sampling variability): 39.36
##
## Test for Heterogeneity:
## Q(df = 2) = 86.8513, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.3791 0.0269 14.0789 <.0001 0.3263 0.4319 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4510 -2.9020 3.0980 -2.9020 27.0980
##
## tau^2 (estimated amount of residual heterogeneity): 0.0032 (SE = 0.0045)
## tau (square root of estimated tau^2 value): 0.0564
## I^2 (residual heterogeneity / unaccounted variability): 98.82%
## H^2 (unaccounted variability / sampling variability): 84.93
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 84.9336, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3313, p-val = 0.5649
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4064 0.0577 7.0440 <.0001 0.2933 0.5195 ***
## continentEurope -0.0404 0.0703 -0.5756 0.5649 -0.1781 0.0973
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3585 -2.7170 3.2830 -2.7170 27.2830
##
## tau^2 (estimated amount of residual heterogeneity): 0.0038 (SE = 0.0055)
## tau (square root of estimated tau^2 value): 0.0618
## I^2 (residual heterogeneity / unaccounted variability): 98.82%
## H^2 (unaccounted variability / sampling variability): 84.50
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 84.5018, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1104, p-val = 0.7397
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2975 0.2484 1.1977 0.2310 -0.1894 0.7844
## mean.age 0.0015 0.0044 0.3322 0.7397 -0.0072 0.0101
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.7718 -7.5436 -1.5436 -7.5436 22.4564
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.0023, p-val = 0.9621
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 86.8490, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1852 0.0222 8.3250 <.0001 0.1416 0.2288 ***
## scale1 0.0201 0.0022 9.3193 <.0001 0.0158 0.0243 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fixed effect model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6147 -7.2294 -3.2294 -5.8431 8.7706
##
## tau^2 (estimated amount of total heterogeneity): 0.0015 (SE = 0.0016)
## tau (square root of estimated tau^2 value): 0.0392
## I^2 (total heterogeneity / total variability): 96.96%
## H^2 (total variability / sampling variability): 32.93
##
## Test for Heterogeneity:
## Q(df = 2) = 94.4415, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0841 0.0232 -3.6265 0.0003 -0.1295 -0.0386 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5085 -3.0171 2.9829 -3.0171 26.9829
##
## tau^2 (estimated amount of residual heterogeneity): 0.0028 (SE = 0.0041)
## tau (square root of estimated tau^2 value): 0.0532
## I^2 (residual heterogeneity / unaccounted variability): 98.94%
## H^2 (unaccounted variability / sampling variability): 94.33
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 94.3332, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0694, p-val = 0.7921
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0960 0.0549 -1.7475 0.0805 -0.2037 0.0117 .
## continentEurope 0.0176 0.0667 0.2635 0.7921 -0.1132 0.1483
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4750 -2.9500 3.0500 -2.9500 27.0500
##
## tau^2 (estimated amount of residual heterogeneity): 0.0030 (SE = 0.0043)
## tau (square root of estimated tau^2 value): 0.0550
## I^2 (residual heterogeneity / unaccounted variability): 98.74%
## H^2 (unaccounted variability / sampling variability): 79.66
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 79.6627, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0029, p-val = 0.9569
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0722 0.2221 -0.3250 0.7452 -0.5075 0.3632
## mean.age -0.0002 0.0040 -0.0541 0.9569 -0.0080 0.0076
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.8379 -5.6757 0.3243 -5.6757 24.3243
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0102
## I^2 (residual heterogeneity / unaccounted variability): 51.53%
## H^2 (unaccounted variability / sampling variability): 2.06
## R^2 (amount of heterogeneity accounted for): 93.26%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.0630, p-val = 0.1509
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 20.8789, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0820 0.0374 2.1926 0.0283 0.0087 0.1554 *
## scale1 -0.0175 0.0038 -4.5693 <.0001 -0.0250 -0.0100 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.5876 -7.1753 -3.1753 -5.7890 8.8247
##
## tau^2 (estimated amount of total heterogeneity): 0.0016 (SE = 0.0017)
## tau (square root of estimated tau^2 value): 0.0397
## I^2 (total heterogeneity / total variability): 97.06%
## H^2 (total variability / sampling variability): 33.99
##
## Test for Heterogeneity:
## Q(df = 2) = 97.5929, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0847 0.0235 -3.6052 0.0003 -0.1307 -0.0386 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4936 -2.9871 3.0129 -2.9871 27.0129
##
## tau^2 (estimated amount of residual heterogeneity): 0.0029 (SE = 0.0042)
## tau (square root of estimated tau^2 value): 0.0541
## I^2 (residual heterogeneity / unaccounted variability): 98.97%
## H^2 (unaccounted variability / sampling variability): 97.46
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 97.4647, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0675, p-val = 0.7951
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0966 0.0557 -1.7342 0.0829 -0.2058 0.0126 .
## continentEurope 0.0176 0.0677 0.2597 0.7951 -0.1151 0.1502
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4608 -2.9216 3.0784 -2.9216 27.0784
##
## tau^2 (estimated amount of residual heterogeneity): 0.0031 (SE = 0.0045)
## tau (square root of estimated tau^2 value): 0.0558
## I^2 (residual heterogeneity / unaccounted variability): 98.78%
## H^2 (unaccounted variability / sampling variability): 82.24
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 82.2435, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0026, p-val = 0.9596
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0734 0.2252 -0.3260 0.7445 -0.5148 0.3680
## mean.age -0.0002 0.0040 -0.0506 0.9596 -0.0081 0.0077
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.8094 -5.6189 0.3811 -5.6189 24.3811
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0108
## I^2 (residual heterogeneity / unaccounted variability): 54.67%
## H^2 (unaccounted variability / sampling variability): 2.21
## R^2 (amount of heterogeneity accounted for): 92.64%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.2059, p-val = 0.1375
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 19.6293, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0832 0.0390 2.1360 0.0327 0.0069 0.1596 *
## scale1 -0.0177 0.0040 -4.4305 <.0001 -0.0255 -0.0098 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6265 -7.2531 -3.2531 -5.8668 8.7469
##
## tau^2 (estimated amount of total heterogeneity): 0.0015 (SE = 0.0016)
## tau (square root of estimated tau^2 value): 0.0389
## I^2 (total heterogeneity / total variability): 96.99%
## H^2 (total variability / sampling variability): 33.21
##
## Test for Heterogeneity:
## Q(df = 2) = 92.6322, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0871 0.0230 -3.7846 0.0002 -0.1322 -0.0420 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.2291 -6.4582 -2.4582 -5.0719 9.5418
##
## tau^2 (estimated amount of total heterogeneity): 0.0020 (SE = 0.0024)
## tau (square root of estimated tau^2 value): 0.0443
## I^2 (total heterogeneity / total variability): 84.02%
## H^2 (total variability / sampling variability): 6.26
##
## Test for Heterogeneity:
## Q(df = 2) = 13.8450, p-val = 0.0010
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2503 0.0281 -8.9043 <.0001 -0.3054 -0.1952 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5270 -3.0541 2.9459 -3.0541 26.9459
##
## tau^2 (estimated amount of residual heterogeneity): 0.0027 (SE = 0.0039)
## tau (square root of estimated tau^2 value): 0.0523
## I^2 (residual heterogeneity / unaccounted variability): 98.92%
## H^2 (unaccounted variability / sampling variability): 92.62
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 92.6211, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0983, p-val = 0.7538
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1011 0.0539 -1.8741 0.0609 -0.2068 0.0046 .
## continentEurope 0.0205 0.0655 0.3136 0.7538 -0.1078 0.1489
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2976 -2.5951 3.4049 -2.5951 27.4049
##
## tau^2 (estimated amount of residual heterogeneity): 0.0041 (SE = 0.0062)
## tau (square root of estimated tau^2 value): 0.0636
## I^2 (residual heterogeneity / unaccounted variability): 92.69%
## H^2 (unaccounted variability / sampling variability): 13.68
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 13.6766, p-val = 0.0002
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0228, p-val = 0.8801
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2578 0.0684 -3.7680 0.0002 -0.3919 -0.1237 ***
## continentEurope 0.0125 0.0828 0.1509 0.8801 -0.1499 0.1749
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4837 -2.9674 3.0326 -2.9674 27.0326
##
## tau^2 (estimated amount of residual heterogeneity): 0.0030 (SE = 0.0043)
## tau (square root of estimated tau^2 value): 0.0545
## I^2 (residual heterogeneity / unaccounted variability): 98.75%
## H^2 (unaccounted variability / sampling variability): 79.82
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 79.8197, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0102, p-val = 0.9197
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0652 0.2201 -0.2961 0.7672 -0.4967 0.3663
## mean.age -0.0004 0.0039 -0.1008 0.9197 -0.0081 0.0073
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2824 -2.5648 3.4352 -2.5648 27.4352
##
## tau^2 (estimated amount of residual heterogeneity): 0.0041 (SE = 0.0064)
## tau (square root of estimated tau^2 value): 0.0643
## I^2 (residual heterogeneity / unaccounted variability): 91.85%
## H^2 (unaccounted variability / sampling variability): 12.27
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 12.2656, p-val = 0.0005
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0030, p-val = 0.9566
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2637 0.2682 -0.9830 0.3256 -0.7894 0.2621
## mean.age 0.0003 0.0048 0.0544 0.9566 -0.0091 0.0097
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.0391 -6.0782 -0.0782 -6.0782 23.9218
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0063
## I^2 (residual heterogeneity / unaccounted variability): 29.89%
## H^2 (unaccounted variability / sampling variability): 1.43
## R^2 (amount of heterogeneity accounted for): 97.35%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.4264, p-val = 0.2323
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 38.9987, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0814 0.0281 2.8905 0.0038 0.0262 0.1365 **
## scale1 -0.0178 0.0028 -6.2449 <.0001 -0.0234 -0.0122 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.3580 -4.7159 1.2841 -4.7159 25.2841
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0117
## I^2 (residual heterogeneity / unaccounted variability): 25.98%
## H^2 (unaccounted variability / sampling variability): 1.35
## R^2 (amount of heterogeneity accounted for): 93.07%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.3510, p-val = 0.2451
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 8.6026, p-val = 0.0034
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0483 0.0735 -0.6572 0.5110 -0.1923 0.0957
## scale1 -0.0212 0.0072 -2.9330 0.0034 -0.0354 -0.0070 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender interaction model
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.7059 -7.4117 -3.4117 -6.0254 8.5883
##
## tau^2 (estimated amount of total heterogeneity): 0.0014 (SE = 0.0015)
## tau (square root of estimated tau^2 value): 0.0370
## I^2 (total heterogeneity / total variability): 93.41%
## H^2 (total variability / sampling variability): 15.17
##
## Test for Heterogeneity:
## Q(df = 2) = 45.6253, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0958 0.0226 -4.2301 <.0001 -0.1401 -0.0514 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.8847 -5.7694 -1.7694 -4.3831 10.2306
##
## tau^2 (estimated amount of total heterogeneity): 0.0026 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0509
## I^2 (total heterogeneity / total variability): 81.98%
## H^2 (total variability / sampling variability): 5.55
##
## Test for Heterogeneity:
## Q(df = 2) = 13.4925, p-val = 0.0012
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2456 0.0343 -7.1626 <.0001 -0.3128 -0.1784 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.9531 -7.9061 -3.9061 -6.5198 8.0939
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0227
## I^2 (total heterogeneity / total variability): 73.06%
## H^2 (total variability / sampling variability): 3.71
##
## Test for Heterogeneity:
## Q(df = 2) = 5.2178, p-val = 0.0736
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0245 0.0158 1.5473 0.1218 -0.0065 0.0554
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5745 -3.1489 2.8511 -3.1489 26.8511
##
## tau^2 (estimated amount of residual heterogeneity): 0.0025 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0495
## I^2 (residual heterogeneity / unaccounted variability): 97.68%
## H^2 (unaccounted variability / sampling variability): 43.19
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 43.1890, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0509, p-val = 0.8214
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0851 0.0539 -1.5777 0.1146 -0.1907 0.0206
## continentEurope -0.0146 0.0645 -0.2257 0.8214 -0.1410 0.1119
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3471 -2.6943 3.3057 -2.6943 27.3057
##
## tau^2 (estimated amount of residual heterogeneity): 0.0036 (SE = 0.0056)
## tau (square root of estimated tau^2 value): 0.0603
## I^2 (residual heterogeneity / unaccounted variability): 91.75%
## H^2 (unaccounted variability / sampling variability): 12.12
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 12.1193, p-val = 0.0005
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2574, p-val = 0.6119
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2057 0.0848 -2.4249 0.0153 -0.3719 -0.0394 *
## continentEurope -0.0486 0.0958 -0.5073 0.6119 -0.2363 0.1391
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.4999, p-val = 0.4795
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.7179, p-val = 0.0299
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0262 0.0272 -0.9626 0.3357 -0.0795 0.0271
## continentEurope 0.0606 0.0279 2.1721 0.0299 0.0059 0.1153 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6427 -3.2855 2.7145 -3.2855 26.7145
##
## tau^2 (estimated amount of residual heterogeneity): 0.0021 (SE = 0.0031)
## tau (square root of estimated tau^2 value): 0.0460
## I^2 (residual heterogeneity / unaccounted variability): 96.53%
## H^2 (unaccounted variability / sampling variability): 28.83
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 28.8299, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2143, p-val = 0.6434
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1837 0.1930 -0.9523 0.3410 -0.5619 0.1944
## mean.age 0.0016 0.0035 0.4630 0.6434 -0.0052 0.0084
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4989 -2.9978 3.0022 -2.9978 27.0022
##
## tau^2 (estimated amount of residual heterogeneity): 0.0025 (SE = 0.0041)
## tau (square root of estimated tau^2 value): 0.0497
## I^2 (residual heterogeneity / unaccounted variability): 84.49%
## H^2 (unaccounted variability / sampling variability): 6.45
## R^2 (amount of heterogeneity accounted for): 4.71%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 6.4464, p-val = 0.0111
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7450, p-val = 0.3881
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4684 0.2600 -1.8013 0.0717 -0.9780 0.0413 .
## mean.age 0.0042 0.0049 0.8631 0.3881 -0.0053 0.0137
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0131
## I^2 (residual heterogeneity / unaccounted variability): 53.11%
## H^2 (unaccounted variability / sampling variability): 2.13
## R^2 (amount of heterogeneity accounted for): 66.59%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.1328, p-val = 0.1442
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.9348, p-val = 0.0867
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1852 0.0923 2.0067 0.0448 0.0043 0.3662 *
## mean.age -0.0030 0.0018 -1.7131 0.0867 -0.0065 0.0004 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.9326 -3.8653 2.1347 -3.8653 26.1347
##
## tau^2 (estimated amount of residual heterogeneity): 0.0010 (SE = 0.0017)
## tau (square root of estimated tau^2 value): 0.0314
## I^2 (residual heterogeneity / unaccounted variability): 80.51%
## H^2 (unaccounted variability / sampling variability): 5.13
## R^2 (amount of heterogeneity accounted for): 27.85%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 5.1296, p-val = 0.0235
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.5190, p-val = 0.2178
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0245 0.0999 0.2454 0.8061 -0.1713 0.2203
## scale1 -0.0126 0.0102 -1.2325 0.2178 -0.0326 0.0074
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3241 -2.6482 3.3518 -2.6482 27.3518
##
## tau^2 (estimated amount of residual heterogeneity): 0.0023 (SE = 0.0059)
## tau (square root of estimated tau^2 value): 0.0478
## I^2 (residual heterogeneity / unaccounted variability): 55.24%
## H^2 (unaccounted variability / sampling variability): 2.23
## R^2 (amount of heterogeneity accounted for): 11.62%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.2340, p-val = 0.1350
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8570, p-val = 0.3546
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0989 0.1625 -0.6085 0.5429 -0.4174 0.2196
## scale1 -0.0156 0.0168 -0.9258 0.3546 -0.0485 0.0174
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.7678 -3.5355 2.4645 -3.5355 26.4645
##
## tau^2 (estimated amount of residual heterogeneity): 0.0013 (SE = 0.0024)
## tau (square root of estimated tau^2 value): 0.0362
## I^2 (residual heterogeneity / unaccounted variability): 76.89%
## H^2 (unaccounted variability / sampling variability): 4.33
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 4.3264, p-val = 0.0375
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5037, p-val = 0.4779
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1029 0.1177 0.8744 0.3819 -0.1278 0.3337
## scale1 -0.0085 0.0120 -0.7097 0.4779 -0.0321 0.0151
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 16. Meta analytic summary of slope estimates
Intercept-only model
Fixed effect model
Linear model
Linear with gender model
Linear with gender interaction model
Quadratic model
Quadratic with gender model
4.7. Social risk-taking
Intercept only model
Models results:
Fixed effect model
Models results:
Linear model
Models results:
Plot age trajectory:
Linear with gender model
Models results:
Plot age trajectory:
Linear with gender interaction model
Models results:
Plot age trajectory:
